Applied Sciences | Special Issue : Intelligent Detection and Identification System Based on Computer Vision Technology

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thermal infrared remote sensing; deep learning"> <div class="editor-div__content smaller-pictures"> <div class='profile-card-drop' data-dropdown='profile-card-drop303037' data-options='is_hover:true, hover_timeout:5000'> <div class="sciprofiles-link" style="display: inline-block"><div class="sciprofiles-link__link"><img class="sciprofiles-link__image" src="/bundles/mdpisciprofileslink/img/unknown-user.png" style="width: auto; height: 16px; border-radius: 50%;"><span class="sciprofiles-link__name"> Dr. Liqin Cao </span></div></div> </div> <div id="profile-card-drop303037" data-dropdown-content class="f-dropdown content profile-card-content" aria-hidden="true" tabindex="-1"> <div class="profile-card__title "> <div class="sciprofiles-link" style="display: inline-block"><div class="sciprofiles-link__link"><img class="sciprofiles-link__image" src="/bundles/mdpisciprofileslink/img/unknown-user.png" style="width: auto; height: 16px; border-radius: 50%;"><span class="sciprofiles-link__name"> Dr. Liqin Cao </span></div></div> </div> <div class="profile-card__buttons" style="margin-bottom: 10px;"> <a href="https://sciprofiles.com/profile/460264?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name" class="button button--color-inversed" target="_blank"> SciProfiles </a> <a href="https://scilit.com/scholars?q=Liqin%20Cao" class="button button--color-inversed" target="_blank"> Scilit </a> <a href="https://www.preprints.org/search?search1=Liqin%20Cao&field1=authors" class="button button--color-inversed" target="_blank"> Preprints.org </a> <a href="https://scholar.google.com/scholar?q=Liqin%20Cao" class="button button--color-inversed" target="_blank" rels="noopener noreferrer"> Google Scholar </a> </div> </div> <br class="show-for-small-only" /> <a class="inline-spacer toEncode emailCaptcha" href="" data-editor-id="303037">E-Mail</a> <a class="inline-spacer" href="https://pps.whu.edu.cn/info/1359/11512.htm" target="_blank" rel="noopener noreferrer">Website</a> <br/> <i>Guest Editor</i><br> </div> <div style="clear: both;"></div> <div class="editor-div__content smaller-pictures"> Research Center of Graphic Communication and Printing and Packaging, Wuhan University, Wuhan 430079, China<br> <b>Interests:</b> infrared remote sening image target detection; thermal infrared remote sensing; deep learning<br> </div> </div> <div class="generic-item editor-div" data-filter="xuan zhang tsetlin machine; learning automata; mathematical analysis on learning algorithms; deep learning"> <div class="editor-div__content smaller-pictures"> <div class='profile-card-drop' data-dropdown='profile-card-drop303038' data-options='is_hover:true, hover_timeout:5000'> <div class="sciprofiles-link" style="display: inline-block"><div class="sciprofiles-link__link"><img class="sciprofiles-link__image" src="/bundles/mdpisciprofileslink/img/unknown-user.png" style="width: auto; height: 16px; border-radius: 50%;"><span class="sciprofiles-link__name"> Dr. Xuan Zhang </span> </div></div> </div> <div id="profile-card-drop303038" data-dropdown-content class="f-dropdown content profile-card-content" aria-hidden="true" tabindex="-1"> <div class="profile-card__title "> <div class="sciprofiles-link" style="display: inline-block"><div class="sciprofiles-link__link"><img class="sciprofiles-link__image" src="/bundles/mdpisciprofileslink/img/unknown-user.png" style="width: auto; height: 16px; border-radius: 50%;"><span class="sciprofiles-link__name"> Dr. Xuan Zhang </span> </div></div> </div> <div class="profile-card__buttons" style="margin-bottom: 10px;"> <a href="https://scilit.com/scholars?q=Xuan%20Zhang" class="button button--color-inversed" target="_blank"> Scilit </a> <a href="https://www.preprints.org/search?search1=Xuan%20Zhang&field1=authors" class="button button--color-inversed" target="_blank"> Preprints.org </a> <a href="https://scholar.google.com/scholar?q=Xuan%20Zhang" class="button button--color-inversed" target="_blank" rels="noopener noreferrer"> Google Scholar </a> </div> </div> <br class="show-for-small-only" /> <a class="inline-spacer toEncode emailCaptcha" href="" data-editor-id="303038">E-Mail</a> <a class="inline-spacer" href="https://www.norceresearch.no/en/persons/xuan-zhang/17422080" target="_blank" rel="noopener noreferrer">Website</a> <br/> <i>Guest Editor</i><br> </div> <div style="clear: both;"></div> <div class="editor-div__content smaller-pictures"> Department of Technology, NORCE Norwegian Research Centre, 4879 Grimstad, Norway<br> <b>Interests:</b> Tsetlin machine; learning automata; mathematical analysis on learning algorithms; deep learning<br> </div> </div> <div class="generic-item editor-div" data-filter="lifei wei change detection of remote sensing imagery; hyperspectral remote sensing; intelligent agricultural remote sensing"> <div class="editor-div__content smaller-pictures"> <div class='profile-card-drop' data-dropdown='profile-card-drop221602' data-options='is_hover:true, hover_timeout:5000'> <div class="sciprofiles-link" style="display: inline-block"><div class="sciprofiles-link__link"><img class="sciprofiles-link__image" src="/data/editors/editor_221602.png?1682319575" style= "width: auto; height: 16px; border-radius: 50%;"><span class="sciprofiles-link__name"> Prof. Dr. Lifei Wei </span></div></div> </div> <div id="profile-card-drop221602" data-dropdown-content class="f-dropdown content profile-card-content" aria-hidden="true" tabindex="-1"> <div class="profile-card__title "> <div class="sciprofiles-link" style="display: inline-block"><div class="sciprofiles-link__link"><img class="sciprofiles-link__image" src="/data/editors/editor_221602.png?1682319575" style= "width: auto; height: 16px; border-radius: 50%;"><span class="sciprofiles-link__name"> Prof. Dr. Lifei Wei </span></div></div> </div> <div class="profile-card__buttons" style="margin-bottom: 10px;"> <a href="https://sciprofiles.com/profile/253351?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name" class="button button--color-inversed" target="_blank"> SciProfiles </a> <a href="https://scilit.com/scholars?q=Lifei%20Wei" class="button button--color-inversed" target="_blank"> Scilit </a> <a href="https://www.preprints.org/search?search1=Lifei%20Wei&field1=authors" class="button button--color-inversed" target="_blank"> Preprints.org </a> <a href="https://scholar.google.com/scholar?q=Lifei%20Wei" class="button button--color-inversed" target="_blank" rels="noopener noreferrer"> Google Scholar </a> </div> </div> <br class="show-for-small-only" /> <a class="inline-spacer toEncode emailCaptcha" href="" data-editor-id="221602">E-Mail</a> <a class="inline-spacer" href="http://zhxy.hubu.edu.cn/info/1450/8548.htm" target="_blank" rel="noopener noreferrer">Website</a> <br/> <i>Guest Editor</i><br> </div> <div style="clear: both;"></div> <div class="editor-div__content smaller-pictures"> Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China<br> <b>Interests:</b> change detection of remote sensing imagery; hyperspectral remote sensing; intelligent agricultural remote sensing<br> <a href="#" id="editor_contrib_221602" onclick="div_toggle(this.id); return false;">Special Issues, Collections and Topics in MDPI journals</a> <div id="div_editor_contrib_221602" style="display: none"> Special Issue in <a href="/journal/sustainability/special_issues/remote_rensing_urban"> <i>Sustainability</i>: Application of Remote Sensing for Sustainable Development of Urban and Rural Areas</a><br> </div> </div> </div> </div> <h2><a name="info"></a>Special Issue Information</h2> <div> <p>Dear Colleagues,</p> <p>Computer vision is a sector of artificial intelligence that combines the theories and techniques of machine learning, pattern recognition and deep learning to enable computers to see, recognize and analyze things in photos and videos in the same way that people do. There are increasing uses for computer vision in intelligent detection and identification system, and the applications of those techniques include medical lesion detection, food sorting, vehicle detection and tracking, remote sensing image target detection and analysis, etc.</p> <p>This Special Issue is dedicated to disseminating new theories/approaches, especially those using the existing models, concepts, and architectural designs, in intelligent detection and identification based on computer vision technology.</p> <p>Subjects up for discussion in this Special Issue are not limited to modern methods, technologies, and further handling of target detection and identification, but this Issue will also focus on the verification of their properties in different practical fields.</p> <p>Dr. Liqin Cao<br />Dr. Xuan Zhang<br />Prof. Dr. Lifei Wei<br /><em>Guest Editors</em></p> <p><p><strong>Manuscript Submission Information</strong><p> <p>Manuscripts should be submitted online at <a href="https://www.mdpi.com/">www.mdpi.com</a> by <a href="https://www.mdpi.com/user/register/">registering</a> and <a href="https://www.mdpi.com/user/login/">logging in to this website</a>. Once you are registered, <a href="https://susy.mdpi.com/user/manuscripts/upload/?journal=applsci">click here to go to the submission form</a>. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.</p> <p>Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the <a href="https://www.mdpi.com/journal/applsci/instructions">Instructions for Authors</a> page. <a href="https://www.mdpi.com/journal/applsci/"><em>Applied Sciences</em></a> is an international peer-reviewed open access semimonthly journal published by MDPI.</p> <p> Please visit the <a href="https://www.mdpi.com/journal/applsci/instructions">Instructions for Authors</a> page before submitting a manuscript. The <a href="https://www.mdpi.com/about/apc/">Article Processing Charge (APC)</a> for publication in this <a href="https://www.mdpi.com/about/openaccess/">open access</a> journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's <a href="https://www.mdpi.com/authors/english">English editing service</a> prior to publication or during author revisions. </p></p> </div> <h2><a name="keywords"></a>Keywords</h2> <div><ul> <li>target detection and identification</li> <li>computer vision</li> <li>artificial intelligence, machine learning and deep learning</li> <li>learning automata</li> <li>detection dataset</li> </ul></div> <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN" "http://www.w3.org/TR/REC-html40/loose.dtd"> <html><body><h2><a name="benefits"></a>Benefits of Publishing in a Special Issue</h2> <ul> <li>Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.</li> <li>Greater discoverability: Special Issues support the reach and impact of scientific research. 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return false;">Ok</a> </div> </div> <a class="close-reveal-modal" aria-label="Close"> <i class="material-icons">clear</i> </a> </div> </div> <div> <div style="clear: both"></div> </div> </div> </div> <div class="generic-item type-section" id=Research> <h2>Research</h2> <div style="margin-top: 15px;"> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1409202" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 19 pages, 4512 KiB   </span> <a href="/2076-3417/14/11/4895/pdf?version=1717582527" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Lightweight Violence Detection Model Based on 2D CNN with Bi-Directional Motion Attention" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/14/11/4895">Lightweight Violence Detection Model Based on 2D CNN with Bi-Directional Motion Attention</a> <div class="authors"> by <span class="inlineblock "><strong>Jingwen Wang</strong>, </span><span class="inlineblock "><strong>Daqi Zhao</strong>, </span><span class="inlineblock "><strong>Haoming Li</strong> and </span><span class="inlineblock "><strong>Deqiang Wang</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2024</b>, <em>14</em>(11), 4895; <a href="https://doi.org/10.3390/app14114895">https://doi.org/10.3390/app14114895</a> - 5 Jun 2024 </div> <a href="/2076-3417/14/11/4895#metrics">Cited by 2</a> | Viewed by 1352 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> With the widespread deployment of surveillance cameras, automatic violence detection has attracted extensive attention from industry and academia. Though researchers have made great progress in video-based violence detection, it is still a challenging task to realize accurate violence detection in real time, especially <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/14/11/4895/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> With the widespread deployment of surveillance cameras, automatic violence detection has attracted extensive attention from industry and academia. Though researchers have made great progress in video-based violence detection, it is still a challenging task to realize accurate violence detection in real time, especially with limited computing resources. In this paper, we propose a lightweight 2D CNN-based violence detection scheme, which takes advantage of frame-grouping to reduce data redundancy greatly and, meanwhile, enable short-term temporal modeling. In particular, a lightweight 2D CNN, named improved EfficientNet-B0, is constructed by integrating our proposed bi-directional long-term motion attention (Bi-LTMA) module and a temporal shift module (TSM) into the original EfficientNet-B0. The Bi-LTMA takes both spatial and channel dimensions into consideration and captures motion features in both forward and backward directions. The TSM is adopted to realize temporal feature interaction. Moreover, an auxiliary classifier is designed and employed to improve the classification capability and generalization performance of the proposed model. Experiment results demonstrate that the computational cost of the proposed model is 1.21 GFLOPS. Moreover, the proposed scheme achieves accuracies of 100%, 98.5%, 91.67%, and 90.25% on the Movie Fight dataset, the Hockey Fight dataset, the Surveillance Camera dataset, and the RWF-2000 dataset, respectively. <a href="/2076-3417/14/11/4895">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/applsci/special_issues/9A8WPOBT65 ">Intelligent Detection and Identification System Based on Computer Vision Technology</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/14/11/4895/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1409202"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1409202"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1409202" data-cycle-prev="#prev1409202" data-cycle-progressive="#images1409202" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1409202-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-14-04895/article_deploy/html/images/applsci-14-04895-g001-550.jpg?1717582593" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1409202" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1409202-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-04895/article_deploy/html/images/applsci-14-04895-g002-550.jpg?1717582594'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1409202-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-04895/article_deploy/html/images/applsci-14-04895-g003-550.jpg?1717582594'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1409202-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-04895/article_deploy/html/images/applsci-14-04895-g004-550.jpg?1717582595'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1409202-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-04895/article_deploy/html/images/applsci-14-04895-g005-550.jpg?1717582595'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1409202-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-04895/article_deploy/html/images/applsci-14-04895-g006-550.jpg?1717582596'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1409202-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-04895/article_deploy/html/images/applsci-14-04895-g007-550.jpg?1717582597'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1409202-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-04895/article_deploy/html/images/applsci-14-04895-g008-550.jpg?1717582598'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1409202-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-14-04895/article_deploy/html/images/applsci-14-04895-g009-550.jpg?1717582600'><p>Figure 9</p></div></script></div></div><div id="article-1409202-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-14-04895/article_deploy/html/images/applsci-14-04895-g001-550.jpg?1717582593" title=" <strong>Figure 1</strong><br/> <p>The pipeline of the proposed lightweight violence detection scheme. F<sub>1</sub>–F<sub>3T</sub> stand for RGB frames sparsely sampled from the video clip. X<sub>1</sub>–X<sub>3T</sub> denote grayscale frames converted from F<sub>1</sub>–F<sub>3T</sub> by RGB2Gray operation. I<sub>1</sub>–I<sub>T</sub> are 3-channel images obtained by frame-grouping.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/11/4895'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-04895/article_deploy/html/images/applsci-14-04895-g002-550.jpg?1717582594" title=" <strong>Figure 2</strong><br/> <p>The architecture of the EfficientNet-B0.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/11/4895'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-04895/article_deploy/html/images/applsci-14-04895-g003-550.jpg?1717582594" title=" <strong>Figure 3</strong><br/> <p>The architecture of the Inverted Residual block. BN stands for batch normalization, Swish is the activation function used, and SE denotes the Squeeze-and-Excitation attention module.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/11/4895'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-04895/article_deploy/html/images/applsci-14-04895-g004-550.jpg?1717582595" title=" <strong>Figure 4</strong><br/> <p>The architecture of the modified Inverted Residual Block. We insert the Bi-LTMA module before the first convolution block, and the TSM module after the first convolution block.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/11/4895'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-04895/article_deploy/html/images/applsci-14-04895-g005-550.jpg?1717582595" title=" <strong>Figure 5</strong><br/> <p>The structure of the bi-directional long-term motion attention module.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/11/4895'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-04895/article_deploy/html/images/applsci-14-04895-g006-550.jpg?1717582596" title=" <strong>Figure 6</strong><br/> <p>The principle of TSM module.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/11/4895'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-04895/article_deploy/html/images/applsci-14-04895-g007-550.jpg?1717582597" title=" <strong>Figure 7</strong><br/> <p>The architecture of the auxiliary branch.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/11/4895'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-04895/article_deploy/html/images/applsci-14-04895-g008-550.jpg?1717582598" title=" <strong>Figure 8</strong><br/> <p>Normalized confusion matrices on the four datasets used.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/11/4895'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-14-04895/article_deploy/html/images/applsci-14-04895-g009-550.jpg?1717582600" title=" <strong>Figure 9</strong><br/> <p>Grad-CAM results of our model and the baseline model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/14/11/4895'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1286249" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 17 pages, 5607 KiB   </span> <a href="/2076-3417/13/23/12617/pdf?version=1700734644" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Binocular Vision-Based Pole-Shaped Obstacle Detection and Ranging Study" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/13/23/12617">Binocular Vision-Based Pole-Shaped Obstacle Detection and Ranging Study</a> <div class="authors"> by <span class="inlineblock "><strong>Lei Cai</strong>, </span><span class="inlineblock "><strong>Congling Zhou</strong>, </span><span class="inlineblock "><strong>Yongqiang Wang</strong>, </span><span class="inlineblock "><strong>Hao Wang</strong> and </span><span class="inlineblock "><strong>Boyu Liu</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(23), 12617; <a href="https://doi.org/10.3390/app132312617">https://doi.org/10.3390/app132312617</a> - 23 Nov 2023 </div> <a href="/2076-3417/13/23/12617#metrics">Cited by 4</a> | Viewed by 1733 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> (1) Background: In real road scenarios, various complex environmental conditions may occur, including bright lights, nighttime, rain, and snow. In such a complex environment for detecting pole-shaped obstacles, it is easy to lose the feature information. A high rate of leakage detection, false <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/23/12617/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> (1) Background: In real road scenarios, various complex environmental conditions may occur, including bright lights, nighttime, rain, and snow. In such a complex environment for detecting pole-shaped obstacles, it is easy to lose the feature information. A high rate of leakage detection, false positives, and measurement errors are generated as a result. (2) Methods: The first part of this paper utilizes the improved YOLOv5 algorithm to detect and classify pole-shaped obstacles. Then, the identified target frame information is combined with binocular stereo matching to obtain more accurate distance information. (3) Results: The experimental results demonstrate that this method achieves a mean average precision (mAP) of 97.4% for detecting pole-shaped obstacles, which is 3.1% higher than the original model. The image inference time is only 1.6 ms, which is 1.8 ms faster than the original algorithm. Additionally, the model size is only 19.0 MB. Furthermore, the range error of this system is less than 7% within the range of 3–15 m. (4) Conclusions: Therefore, the algorithm not only achieves real-time and accurate identification and classification but also ensures precise measurement within a specific range. Meanwhile, the model is lightweight and better suited for deploying sensing systems. <a href="/2076-3417/13/23/12617">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/applsci/special_issues/9A8WPOBT65 ">Intelligent Detection and Identification System Based on Computer Vision Technology</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/23/12617/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1286249"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1286249"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1286249" data-cycle-prev="#prev1286249" data-cycle-progressive="#images1286249" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1286249-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-12617/article_deploy/html/images/applsci-13-12617-g001-550.jpg?1700734751" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1286249" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1286249-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-12617/article_deploy/html/images/applsci-13-12617-g002-550.jpg?1700734752'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1286249-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-12617/article_deploy/html/images/applsci-13-12617-g003-550.jpg?1700734753'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1286249-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-12617/article_deploy/html/images/applsci-13-12617-g004-550.jpg?1700734754'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1286249-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-12617/article_deploy/html/images/applsci-13-12617-g005-550.jpg?1700734757'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1286249-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-12617/article_deploy/html/images/applsci-13-12617-g006-550.jpg?1700734759'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1286249-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-12617/article_deploy/html/images/applsci-13-12617-g007-550.jpg?1700734762'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1286249-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-12617/article_deploy/html/images/applsci-13-12617-g008-550.jpg?1700734764'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1286249-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-12617/article_deploy/html/images/applsci-13-12617-g009-550.jpg?1700734769'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1286249-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-12617/article_deploy/html/images/applsci-13-12617-g010-550.jpg?1700734771'><p>Figure 10</p></div></script></div></div><div id="article-1286249-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-12617/article_deploy/html/images/applsci-13-12617-g001-550.jpg?1700734751" title=" <strong>Figure 1</strong><br/> <p>Overall workflow diagram.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/23/12617'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-12617/article_deploy/html/images/applsci-13-12617-g002-550.jpg?1700734752" title=" <strong>Figure 2</strong><br/> <p>Cascade Hybrid Attention Mechanism.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/23/12617'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-12617/article_deploy/html/images/applsci-13-12617-g003-550.jpg?1700734753" title=" <strong>Figure 3</strong><br/> <p>Multi-scale feature pyramid network structure.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/23/12617'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-12617/article_deploy/html/images/applsci-13-12617-g004-550.jpg?1700734754" title=" <strong>Figure 4</strong><br/> <p>MFMAM-YOLOv5s network architecture diagram. (<b>a</b>) represents the main network diagram, while (<b>b</b>) illustrates the internal structure of CSPStage.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/23/12617'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-12617/article_deploy/html/images/applsci-13-12617-g005-550.jpg?1700734757" title=" <strong>Figure 5</strong><br/> <p>Stereo calibration chart.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/23/12617'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-12617/article_deploy/html/images/applsci-13-12617-g006-550.jpg?1700734759" title=" <strong>Figure 6</strong><br/> <p>Categorization of detections. (<b>a</b>) Electric pole; (<b>b</b>) Surveillance pole; (<b>c</b>) Fixed traffic signal pole; (<b>d</b>) Mobile traffic signal pole; (<b>e</b>) Traffic cone; (<b>f</b>) Fixed road pile pole; (<b>g</b>) Mobile road pile pole; (<b>h</b>) Street lamp pole; (<b>i</b>) Traffic sign pole.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/23/12617'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-12617/article_deploy/html/images/applsci-13-12617-g007-550.jpg?1700734762" title=" <strong>Figure 7</strong><br/> <p>Data augmentation technology. (<b>a</b>) Original image; (<b>b</b>) Flip horizontal; (<b>c</b>) Random rotation; (<b>d</b>) Fuzzy processing; (<b>e</b>) Random brightness; (<b>f</b>) Gaussian noise.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/23/12617'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-12617/article_deploy/html/images/applsci-13-12617-g008-550.jpg?1700734764" title=" <strong>Figure 8</strong><br/> <p>Comparative analysis of model evaluation indicators. (<b>a</b>) Overall loss convergence curves; (<b>b</b>) mAP@0.5 Convergence curves.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/23/12617'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-12617/article_deploy/html/images/applsci-13-12617-g009-550.jpg?1700734769" title=" <strong>Figure 9</strong><br/> <p>Comparison Results Between Different Algorithms. (<b>a</b>) YOLOv5s; (<b>b</b>) YOLOv7; (<b>c</b>) YOLOv8s; (<b>d</b>) MFMAM-YOLOv5s.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/23/12617'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-12617/article_deploy/html/images/applsci-13-12617-g010-550.jpg?1700734771" title=" <strong>Figure 10</strong><br/> <p>Statistical Comparison Chart of Ranging Experiment Results. (<b>a</b>) Comparison of actual distance and test distance; (<b>b</b>) Absolute error statistics chart; (<b>c</b>) Relative error statistics.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/23/12617'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1199541" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 13 pages, 4935 KiB   </span> <a href="/2076-3417/13/14/8450/pdf?version=1689941136" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Machine Vision Algorithm for Identifying Packaging Components of HN-3 Arterial Blood Sample Collector" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/13/14/8450">Machine Vision Algorithm for Identifying Packaging Components of HN-3 Arterial Blood Sample Collector</a> <div class="authors"> by <span class="inlineblock "><strong>Zhendong Shang</strong>, </span><span class="inlineblock "><strong>Qinzhang Wei</strong> and </span><span class="inlineblock "><strong>Zhaoying Li</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(14), 8450; <a href="https://doi.org/10.3390/app13148450">https://doi.org/10.3390/app13148450</a> - 21 Jul 2023 </div> <a href="/2076-3417/13/14/8450#metrics">Cited by 1</a> | Viewed by 1128 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The arterial blood sample collector produced in large quantities often fails to meet the requirements due to missing components in the packaging bag, and traditional manual detection methods are both inefficient and inaccurate. To solve this problem, a PyCharm-integrated development environment was used <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/14/8450/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The arterial blood sample collector produced in large quantities often fails to meet the requirements due to missing components in the packaging bag, and traditional manual detection methods are both inefficient and inaccurate. To solve this problem, a PyCharm-integrated development environment was used to study image processing and recognition algorithms for identifying components inside the packaging bag of the HN-3 arterial blood sample collector. The machine vision system was used to capture images of the packaging bags of the HN-3 Arterial blood sample collector. Template matching was employed to extract the packaging ROI, and the threshold segmentation method in the HSV color model was used to extract material features based on the packaging ROI. Morphological processing algorithms such as dilation or erosion were used to enhance the connectivity of the extracted features. The existence of components was determined by setting thresholds for the connected domain area or length. The results of the recognition experiment show that the false detection rate is 0.2%, the missed detection rate is 0%, and the average image processing time per product is no more than 39 ms. Compared with manual recognition methods, the efficiency and accuracy have been improved by 36.5 times and 2.3%, respectively. The experimental results confirm the effectiveness of the image processing algorithm. <a href="/2076-3417/13/14/8450">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/applsci/special_issues/9A8WPOBT65 ">Intelligent Detection and Identification System Based on Computer Vision Technology</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/14/8450/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1199541"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1199541"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1199541" data-cycle-prev="#prev1199541" data-cycle-progressive="#images1199541" 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src='https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g003-550.jpg?1689941216'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1199541-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g004-550.jpg?1689941212'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1199541-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g005-550.jpg?1689941215'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1199541-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g006-550.jpg?1689941219'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1199541-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g007-550.jpg?1689941211'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1199541-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g008-550.jpg?1689941208'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1199541-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g009a-550.jpg?1689941215'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1199541-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g009b-550.jpg?1689941215'><p>Figure 9 Cont.</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1199541-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g010-550.jpg?1689941210'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1199541-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g011-550.jpg?1689941219'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1199541-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g012-550.jpg?1689941211'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='13' data-target='article-1199541-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g013-550.jpg?1689941217'><p>Figure 13</p></div> --- <div class='openpopupgallery' data-imgindex='14' data-target='article-1199541-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g014-550.jpg?1689941218'><p>Figure 14</p></div> --- <div class='openpopupgallery' data-imgindex='15' data-target='article-1199541-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g015-550.jpg?1689941214'><p>Figure 15</p></div></script></div></div><div id="article-1199541-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g001-550.jpg?1689941218" title=" <strong>Figure 1</strong><br/> <p>Composition of arterial blood collector.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g002-550.jpg?1689941209" title=" <strong>Figure 2</strong><br/> <p>Template matching result. (<b>a</b>) Source image; (<b>b</b>) template image; (<b>c</b>) ROI image.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g003-550.jpg?1689941216" title=" <strong>Figure 3</strong><br/> <p>HSV three components. (<b>a</b>) <span class="html-italic">H</span> component; (<b>b</b>) <span class="html-italic">S</span> component; (<b>c</b>) <span class="html-italic">V</span> component.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g004-550.jpg?1689941212" title=" <strong>Figure 4</strong><br/> <p>Threshold segmentation image. (<b>a</b>) The result of channel separation for <span class="html-italic">H</span>; (<b>b</b>) the result of channel separation for <span class="html-italic">S</span>; (<b>c</b>) the result of channel separation for <span class="html-italic">V</span>; (<b>d</b>) the result of comprehensive segmentation.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g005-550.jpg?1689941215" title=" <strong>Figure 5</strong><br/> <p>Morphologically processed image.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g006-550.jpg?1689941219" title=" <strong>Figure 6</strong><br/> <p>Threshold segmentation of the <span class="html-italic">S</span> component. (<b>a</b>) Not adhered; (<b>b</b>) adhered.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g007-550.jpg?1689941211" title=" <strong>Figure 7</strong><br/> <p>Segmentation of the <span class="html-italic">H</span> component. (<b>a</b>) Not adhered; (<b>b</b>) adhered.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g008-550.jpg?1689941208" title=" <strong>Figure 8</strong><br/> <p>Histogram of gray values in <span class="html-italic">V</span> component. (<b>a</b>) Not adhered; (<b>b</b>) adhered.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g009a-550.jpg?1689941215" title=" <strong>Figure 9</strong><br/> <p>Image processing result. (<b>a</b>) Non-adhesive comprehensive segmentation; (<b>b</b>) adhesive comprehensive segmentation; (<b>c</b>) non-adhesive morphological processing; (<b>d</b>) adhesive morphological processing.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g009b-550.jpg?1689941215" title=" <strong>Figure 9 Cont.</strong><br/> <p>Image processing result. (<b>a</b>) Non-adhesive comprehensive segmentation; (<b>b</b>) adhesive comprehensive segmentation; (<b>c</b>) non-adhesive morphological processing; (<b>d</b>) adhesive morphological processing.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g010-550.jpg?1689941210" title=" <strong>Figure 10</strong><br/> <p>Segmentation result of blood collection needle head based on packaging ROI.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g011-550.jpg?1689941219" title=" <strong>Figure 11</strong><br/> <p>Blood collection needle head ROI segmentation process. (<b>a</b>) Blood collection needle head ROI; (<b>b</b>) threshold segmentation image; (<b>c</b>) morphological processing image.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g012-550.jpg?1689941211" title=" <strong>Figure 12</strong><br/> <p>Test system composition.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g013-550.jpg?1689941217" title=" <strong>Figure 13</strong><br/> <p>Capture control system structure.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g014-550.jpg?1689941218" title=" <strong>Figure 14</strong><br/> <p>Camera and light source mounting architecture.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8450'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08450/article_deploy/html/images/applsci-13-08450-g015-550.jpg?1689941214" title=" <strong>Figure 15</strong><br/> <p>Line chart of connected domain area or diagonal length statistics. (<b>a</b>) Needle plug; (<b>b</b>) drawbar piston and the cone seal; (<b>c</b>) blood sampling needle.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8450'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1193113" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <a data-dropdown="drop-supplementary-1193113" aria-controls="drop-supplementary-1193113" aria-expanded="false" title="Supplementary Material"> <i class="material-icons">attachment</i> </a> <div id="drop-supplementary-1193113" class="f-dropdown label__btn__dropdown label__btn__dropdown--wide" data-dropdown-content aria-hidden="true" tabindex="-1"> Supplementary material: <br/> <a href="/2076-3417/13/14/8146/s1?version=1689239436"> Supplementary File 1 (ZIP, 717 KiB) </a><br/> </div> </div> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 16 pages, 9288 KiB   </span> <a href="/2076-3417/13/14/8146/pdf?version=1689239435" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Convolutional Neural Network for Segmenting Micro-X-ray Computed Tomography Images of Wood Cellular Structures" data-journal="applsci"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/2076-3417/13/14/8146">Convolutional Neural Network for Segmenting Micro-X-ray Computed Tomography Images of Wood Cellular Structures</a> <div class="authors"> by <span class="inlineblock "><strong>Xavier Arzola-Villegas</strong>, </span><span class="inlineblock "><strong>Carlos Báez</strong>, </span><span class="inlineblock "><strong>Roderic Lakes</strong>, </span><span class="inlineblock "><strong>Donald S. Stone</strong>, </span><span class="inlineblock "><strong>Jane O’Dell</strong>, </span><span class="inlineblock "><strong>Pavel Shevchenko</strong>, </span><span class="inlineblock "><strong>Xianghui Xiao</strong>, </span><span class="inlineblock "><strong>Francesco De Carlo</strong> and </span><span class="inlineblock "><strong>Joseph E. Jakes</strong></span> </div> <div class="color-grey-dark"> <em>Appl. Sci.</em> <b>2023</b>, <em>13</em>(14), 8146; <a href="https://doi.org/10.3390/app13148146">https://doi.org/10.3390/app13148146</a> - 13 Jul 2023 </div> <a href="/2076-3417/13/14/8146#metrics">Cited by 4</a> | Viewed by 2194 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> To further enhance the performance of wood products, improved tools are needed to study in situ cellular scale phenomena like mechanical deformations and moisture swelling. Micro-X-ray computed tomography (μXCT) using brilliant synchrotron light sources now has the spatial and temporal resolution for real-time <a href="#" data-counterslink = "https://www.mdpi.com/2076-3417/13/14/8146/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> To further enhance the performance of wood products, improved tools are needed to study in situ cellular scale phenomena like mechanical deformations and moisture swelling. Micro-X-ray computed tomography (μXCT) using brilliant synchrotron light sources now has the spatial and temporal resolution for real-time visualization of phenomena in three-dimensional cellular structures. However, the tradeoff for speed includes the loss of intensity contrast between different types of materials within the imaged structure, such as cell wall and air in wood. This loss of contrast prevents traditional histogram-based segmentation methods from being used effectively. A new convolutional neural network (CNN) approach was therefore developed to segment fast μXCT images of wood into cell wall and air volumes. The fast μXCT and segmentation were demonstrated in the study of moisture swelling in loblolly pine (<i>Pinus taeda</i>) earlywood and latewood cellular structures conditioned at 0%, 33%, 75%, and 95% relative humidity (RH). The CNN segmentation results had a mean intersection over union (IoU) metric accuracy of 96%. Initial analysis of the swelling in the latewood revealed cell walls swelled about 25% when conditioned from 0% to 95% RH. Additionally, the widths of ray cell lumina in the transverse plane of latewood could be observed to increase at higher RH. The segmentation method presented here will facilitate future quantitative analyses in in situ μXCT studies of wood and other similar cellular materials. <a href="/2076-3417/13/14/8146">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/applsci/special_issues/9A8WPOBT65 ">Intelligent Detection and Identification System Based on Computer Vision Technology</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/2076-3417/13/14/8146/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1193113"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1193113"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1193113" data-cycle-prev="#prev1193113" data-cycle-progressive="#images1193113" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1193113-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/applsci/applsci-13-08146/article_deploy/html/images/applsci-13-08146-g001-550.jpg?1689239540" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1193113" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1193113-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08146/article_deploy/html/images/applsci-13-08146-g002-550.jpg?1689239520'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1193113-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08146/article_deploy/html/images/applsci-13-08146-g003-550.jpg?1689239517'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1193113-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08146/article_deploy/html/images/applsci-13-08146-g004-550.jpg?1689239537'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1193113-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08146/article_deploy/html/images/applsci-13-08146-g005-550.jpg?1689239526'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1193113-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08146/article_deploy/html/images/applsci-13-08146-g006-550.jpg?1689239531'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1193113-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08146/article_deploy/html/images/applsci-13-08146-g007-550.jpg?1689239542'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1193113-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08146/article_deploy/html/images/applsci-13-08146-g008-550.jpg?1689239533'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1193113-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08146/article_deploy/html/images/applsci-13-08146-g009-550.jpg?1689239524'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1193113-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/applsci/applsci-13-08146/article_deploy/html/images/applsci-13-08146-g010-550.jpg?1689239528'><p>Figure 10</p></div></script></div></div><div id="article-1193113-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/applsci/applsci-13-08146/article_deploy/html/images/applsci-13-08146-g001-550.jpg?1689239540" title=" <strong>Figure 1</strong><br/> <p>Illustration of softwood cellular structure. Softwood is an anisotropic cellular material defined by the radial, tangential, and longitudinal directions [<a href="#B10-applsci-13-08146" class="html-bibr">10</a>]. It is mainly composed of longitudinal tracheids and ray cells. Pits are small openings between cells that facilitate intercellular water transport. A single longitudinal tracheid can be thought of as a hollow tube with the open space called a lumen. The cell wall is a multilamellar structure with secondary cell wall layers that are nanofiber-reinforced composites consisting of helically wound cellulose microfibrils embedded in a matrix of amorphous cellulose, hemicelluloses, and lignin [<a href="#B6-applsci-13-08146" class="html-bibr">6</a>]. Wood polymers are hygroscopic, and as a hygroscopic material wood’s properties and performance depend on the amount of water in the wood [<a href="#B7-applsci-13-08146" class="html-bibr">7</a>]. Growth rings consist of earlywood and latewood layers. Latewood is denser than earlywood because its tracheids have smaller lumina and thicker cell walls.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8146'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08146/article_deploy/html/images/applsci-13-08146-g002-550.jpg?1689239520" title=" <strong>Figure 2</strong><br/> <p>Custom-built relative humidity (RH) chamber for APS beamline 2-BM-B. The chamber separated into two pieces to facilitate sample changes. The top of the chamber consisted of 6.3 cm length of a Precision Paper Tube Company (Wheeling, IL, USA) Kapton<sup>©</sup> tube with 127 µm thick walls and 4.45 cm outside diameter. The Kapton<sup>©</sup> tube had machined aluminum caps at each end. The bottom consisted of machined aluminum with a Ted Pella™ (Mountain Lakes Blvd, Redding, CA, USA) pin mount sample holder that was secured in the chamber with a set screw, a hose barb connector for the RH generator connection, a Sensirion™ (Staefa, Switzerland) SHT1x relative humidity and temperature sensor to monitor the conditions inside the chamber, and a ThorLabs™ (Sparta Ave, Newton, NJ, USA) kinematic mount to attach the RH chamber to the beamline rotation stage.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8146'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08146/article_deploy/html/images/applsci-13-08146-g003-550.jpg?1689239517" title=" <strong>Figure 3</strong><br/> <p>Comparison between the (<b>a</b>) filtered back projection (FBP) and the (<b>b</b>) conjugate gradient least squares (CGLS) reconstruction algorithms. The images are 8-bit and no filters were applied post-reconstruction. The line profiles show the noise along the white lines for each algorithm with regions of void and cell walls indicated with v and c, respectively. Intensity bar has units of grayscale values.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8146'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08146/article_deploy/html/images/applsci-13-08146-g004-550.jpg?1689239537" title=" <strong>Figure 4</strong><br/> <p>Representative 8-bit reconstruction slices from 1 mm cross-section (<b>a</b>) latewood and (<b>b</b>) earlywood specimens with their corresponding histograms in (<b>c</b>) and (<b>d</b>), respectively. The close-up views in (<b>e</b>) and (<b>f</b>) are from the white boxes in (<b>a</b>) and (<b>b</b>), respectively. Intensity bars have units of grayscale values.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8146'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08146/article_deploy/html/images/applsci-13-08146-g005-550.jpg?1689239526" title=" <strong>Figure 5</strong><br/> <p>Convolutional neural network (CNN) architecture diagram used in this work. This CNN was an adaptation from the U-Net architecture designed by Ronneberger and coworkers [<a href="#B26-applsci-13-08146" class="html-bibr">26</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8146'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08146/article_deploy/html/images/applsci-13-08146-g006-550.jpg?1689239531" title=" <strong>Figure 6</strong><br/> <p>Comparison of (<b>a</b>,<b>d</b>,<b>g</b>) grayscale, (<b>b</b>,<b>e</b>,<b>h</b>) ground-truth binary, and (<b>c</b>,<b>f</b>,<b>i</b>) convolutional neural network (CNN) binary images. The grayscale images are 8-bit with no filters applied post-reconstruction. Intensity bar has units of grayscale values.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8146'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08146/article_deploy/html/images/applsci-13-08146-g007-550.jpg?1689239542" title=" <strong>Figure 7</strong><br/> <p>Comparisons of (<b>a</b>,<b>c</b>,<b>e</b>) grayscale and (<b>b</b>,<b>d</b>,<b>f</b>) convolutional neural networks (CNN) binary images showing streak artifacts. The grayscale images are 8-bit with no filters applied post-reconstruction. Intensity bar has units of grayscale values.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8146'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08146/article_deploy/html/images/applsci-13-08146-g008-550.jpg?1689239533" title=" <strong>Figure 8</strong><br/> <p>Comparison of (<b>a</b>)grayscale and (<b>b</b>) convolutional neural network (CNN) binary images showing the effects of blurring noise in a 0.5 mm cross-section latewood specimen. The grayscale images are 8-bit with no filters applied post-reconstruction. Intensity bar has units of grayscale values.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8146'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08146/article_deploy/html/images/applsci-13-08146-g009-550.jpg?1689239524" title=" <strong>Figure 9</strong><br/> <p>Three-dimensional renderings from a 1 mm cross-section latewood specimen conditioned at 0% relative humidity. The full image (<b>a</b>) is from 1400 image slices corresponding to a total length of 910 μm. The sub volumes show virtually separated (<b>b</b>) longitudinal tracheid cell walls, (<b>c</b>) longitudinal tracheid lumina, and (<b>d</b>) ray cell lumina.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8146'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/applsci/applsci-13-08146/article_deploy/html/images/applsci-13-08146-g010-550.jpg?1689239528" title=" <strong>Figure 10</strong><br/> <p>Superimposed 1 mm cross-sections of a latewood sample conditioned at 0% and 95% relative humidity.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/2076-3417/13/14/8146'>Full article</a></strong> "></a></div> </div> </div> <div class="row footer"> <div class="listing-select-options"> <div class="columns small-12"> <div class="select generic-item"> <a href="#" class="export-options-show export-element export-expanded"> Show export options <i class="material-icons">expand_more</i> </a> <a href="#" class="export-options-show export-element"> Show export options <i class="material-icons">expand_less</i> </a> </div> <div class="listing-export-options export-element"> <div class="export-element" style="margin-top: 10px; margin-bottom: 10px;"> <input type="checkbox" class="selector selectUnselectAll bb-checkbox" id="selectUnselectAll" data-select-all="article-listing"> <div class="indented bb-indented"> Select all </div> </div> <div class="indented1"> <span style="display: inline-block; margin-right: 15px; margin-bottom: 10px;">Export citation of selected articles as:</span> <br class="show-for-medium-down" /> <div class="listing-export"> <select class="chosen-select inline" name="format_bottom"> <option value="plaintext"> Plain Text </option> <option value="bibtex"> BibTeX </option> <option value="bibtex_no_abstract"> BibTeX (without abstracts) </option> <option value="endnote"> Endnote </option> <option value="endnote_no_abstract"> Endnote (without abstracts) </option> <option value="tab_delimited"> Tab-delimited </option> <option value="ris"> RIS </option> </select> </div> <input type="submit" value="Export" class="articleBrowserSubmit button button--color-inversed" id="articleBrowserExport_bottom"> </div> <div style="border-top: 1px solid #ededed; height: 5px; margin-top: 15px;">   </div> </div> <a id="link-export-modal-nothing-selected" data-reveal-id="export-modal-nothing-selected" style="display: none;"></a> <div id="export-modal-nothing-selected" class="reveal-modal reveal-modal-new reveal-modal-new--small" data-reveal aria-labelledby="modalTitle" aria-hidden="true" role="dialog"> <div class="row"> <div class="small-12 columns"> <h2>Error</h2> Oops... you haven't selected anything for export. </div> <div class="small-12 columns"> <a class="button button--color" onclick="$(this).closest('.reveal-modal-new').foundation('reveal', 'close'); return false;">Ok</a> </div> </div> <a class="close-reveal-modal" aria-label="Close"> <i class="material-icons">clear</i> </a> </div> </div> <div> <div class="columns large-12 medium-12 small-12"> Displaying articles 1-4 </div> <div style="clear: both"></div> </div> </div> </div> </div> </form> </div> <span data-special-issue-id="170966"></span> </div> </div> </div> </div> </div> </section> <div id="footer"> <div class="journal-info"> <span> <em><a class="Var_JournalInfo" href="/journal/applsci">Appl. 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Applied Sciences | Special Issue : Intelligent Detection and Identification System Based on Computer Vision Technology