MGL-YOLO: A Lightweight Barcode Target Detection Algorithm

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} div.type-section h2 { font-size: 20px; line-height: 26px; font-weight: 300; } div.type-section h3 { margin-left: 15px; margin-bottom: 0px; font-weight: 300; } .journal-tabs .tab-title.active a { } </style> <link rel="stylesheet" href="https://pub.mdpi-res.com/assets/css/slick.css?f38b2db10e01b157?1732706938"> <meta name="title" content="MGL-YOLO: A Lightweight Barcode Target Detection Algorithm"> <meta name="description" content="Due to the critical importance of one-dimensional barcode detection in logistics, retail, and manufacturing, which has become a key issue affecting operational efficiency, researchers have shown increasing interest in this area. However, deploying deep convolutional neural networks on embedded and some edge devices is very challenging due to limited storage space and computational resources. To address this issue, this paper proposes MGL-YOLO, a lightweight one-dimensional barcode detection network based on an improved YOLOv8, which aims to achieve a high detection accuracy at low computational cost. First, a new multi-scale group convolution (MSGConv) is designed and integrated into the C2f module to construct the MSG-C2f feature extraction module. By replacing the C2f module in the P5 layer of the backbone network, the ability to extract multi-scale feature information is enhanced. Secondly, a feature extraction module, Group RepConv Cross Stage Partial Efficient Long-Range Attention Network (GRCE), is designed to optimize the feature extraction capability of the C2f modules in the neck section, offering significant advantages in multi-scale characteristics and complexity adjustment. Finally, a Lightweight Shared Multi-Scale Detection Head (LSMD) is proposed, which improves the model’s detection accuracy and adaptability while reducing the model’s parameter size and computational complexity. Experimental results show that the proposed algorithm increases MAP50 and MAP50.95 by 2.57% and 2.31%, respectively, compared to YOLOv8, while reducing parameter size and computational cost by 36.21% and 34.15%, respectively. Moreover, it also demonstrates advantages in average precision compared to other object detection networks, proving the effectiveness of MGL-YOLO for one-dimensional barcode detection in complex backgrounds." > <link rel="image_src" href="https://pub.mdpi-res.com/img/journals/sensors-logo.png?8600e93ff98dbf14" > <meta name="dc.title" content="MGL-YOLO: A Lightweight Barcode Target Detection Algorithm"> <meta name="dc.creator" content="Yuanhao Qu"> <meta name="dc.creator" content="Fengshou Zhang"> <meta name="dc.type" content="Article"> <meta name="dc.source" content="Sensors 2024, Vol. 24, Page 7590"> <meta name="dc.date" content="2024-11-27"> <meta name ="dc.identifier" content="10.3390/s24237590"> <meta name="dc.publisher" content="Multidisciplinary Digital Publishing Institute"> <meta name="dc.rights" content="http://creativecommons.org/licenses/by/3.0/"> <meta name="dc.format" content="application/pdf" > <meta name="dc.language" content="en" > <meta name="dc.description" content="Due to the critical importance of one-dimensional barcode detection in logistics, retail, and manufacturing, which has become a key issue affecting operational efficiency, researchers have shown increasing interest in this area. However, deploying deep convolutional neural networks on embedded and some edge devices is very challenging due to limited storage space and computational resources. To address this issue, this paper proposes MGL-YOLO, a lightweight one-dimensional barcode detection network based on an improved YOLOv8, which aims to achieve a high detection accuracy at low computational cost. First, a new multi-scale group convolution (MSGConv) is designed and integrated into the C2f module to construct the MSG-C2f feature extraction module. By replacing the C2f module in the P5 layer of the backbone network, the ability to extract multi-scale feature information is enhanced. Secondly, a feature extraction module, Group RepConv Cross Stage Partial Efficient Long-Range Attention Network (GRCE), is designed to optimize the feature extraction capability of the C2f modules in the neck section, offering significant advantages in multi-scale characteristics and complexity adjustment. Finally, a Lightweight Shared Multi-Scale Detection Head (LSMD) is proposed, which improves the model’s detection accuracy and adaptability while reducing the model’s parameter size and computational complexity. Experimental results show that the proposed algorithm increases MAP50 and MAP50.95 by 2.57% and 2.31%, respectively, compared to YOLOv8, while reducing parameter size and computational cost by 36.21% and 34.15%, respectively. Moreover, it also demonstrates advantages in average precision compared to other object detection networks, proving the effectiveness of MGL-YOLO for one-dimensional barcode detection in complex backgrounds." > <meta name="dc.subject" content="one-dimensional barcode recognition" > <meta name="dc.subject" content="target detection" > <meta name="dc.subject" content="lightweight network" > <meta name="dc.subject" content="feature extraction" > <meta name="dc.subject" content="deep learning" > <meta name ="prism.issn" content="1424-8220"> <meta name ="prism.publicationName" content="Sensors"> <meta name ="prism.publicationDate" content="2024-11-27"> <meta name ="prism.volume" content="24"> <meta name ="prism.number" content="23"> <meta name ="prism.section" content="Article" > <meta name ="prism.startingPage" content="7590" > <meta name="citation_issn" content="1424-8220"> <meta name="citation_journal_title" content="Sensors"> <meta name="citation_publisher" content="Multidisciplinary Digital Publishing Institute"> <meta name="citation_title" content="MGL-YOLO: A Lightweight Barcode Target Detection Algorithm"> <meta name="citation_publication_date" content="2024/1"> <meta name="citation_online_date" content="2024/11/27"> <meta name="citation_volume" content="24"> <meta name="citation_issue" content="23"> <meta name="citation_firstpage" content="7590"> <meta name="citation_author" content="Qu, Yuanhao"> <meta name="citation_author" content="Zhang, Fengshou"> <meta name="citation_doi" content="10.3390/s24237590"> <meta name="citation_id" content="mdpi-s24237590"> <meta name="citation_abstract_html_url" content="https://www.mdpi.com/1424-8220/24/23/7590"> <meta name="citation_pdf_url" content="https://www.mdpi.com/1424-8220/24/23/7590/pdf?version=1732723427"> <link rel="alternate" type="application/pdf" title="PDF Full-Text" href="https://www.mdpi.com/1424-8220/24/23/7590/pdf?version=1732723427"> <meta name="fulltext_pdf" content="https://www.mdpi.com/1424-8220/24/23/7590/pdf?version=1732723427"> <meta name="twitter:card" content="summary" /> <meta name="twitter:site" content="@MDPIOpenAccess" /> <meta name="twitter:image" content="https://pub.mdpi-res.com/img/journals/sensors-logo-social.png?8600e93ff98dbf14" /> <meta property="fb:app_id" content="131189377574"/> <meta property="og:site_name" content="MDPI"/> <meta property="og:type" content="article"/> <meta property="og:url" content="https://www.mdpi.com/1424-8220/24/23/7590" /> <meta property="og:title" content="MGL-YOLO: A Lightweight Barcode Target Detection Algorithm" /> <meta property="og:description" content="Due to the critical importance of one-dimensional barcode detection in logistics, retail, and manufacturing, which has become a key issue affecting operational efficiency, researchers have shown increasing interest in this area. However, deploying deep convolutional neural networks on embedded and some edge devices is very challenging due to limited storage space and computational resources. To address this issue, this paper proposes MGL-YOLO, a lightweight one-dimensional barcode detection network based on an improved YOLOv8, which aims to achieve a high detection accuracy at low computational cost. First, a new multi-scale group convolution (MSGConv) is designed and integrated into the C2f module to construct the MSG-C2f feature extraction module. By replacing the C2f module in the P5 layer of the backbone network, the ability to extract multi-scale feature information is enhanced. Secondly, a feature extraction module, Group RepConv Cross Stage Partial Efficient Long-Range Attention Network (GRCE), is designed to optimize the feature extraction capability of the C2f modules in the neck section, offering significant advantages in multi-scale characteristics and complexity adjustment. Finally, a Lightweight Shared Multi-Scale Detection Head (LSMD) is proposed, which improves the model’s detection accuracy and adaptability while reducing the model’s parameter size and computational complexity. Experimental results show that the proposed algorithm increases MAP50 and MAP50.95 by 2.57% and 2.31%, respectively, compared to YOLOv8, while reducing parameter size and computational cost by 36.21% and 34.15%, respectively. Moreover, it also demonstrates advantages in average precision compared to other object detection networks, proving the effectiveness of MGL-YOLO for one-dimensional barcode detection in complex backgrounds." /> <meta property="og:image" content="https://pub.mdpi-res.com/img/journals/sensors-logo-social.png?8600e93ff98dbf14" /> <link rel="alternate" type="application/rss+xml" title="MDPI Publishing - Latest articles" href="https://www.mdpi.com/rss"> <meta name="google-site-verification" content="PxTlsg7z2S00aHroktQd57fxygEjMiNHydKn3txhvwY"> <meta name="facebook-domain-verification" content="mcoq8dtq6sb2hf7z29j8w515jjoof7" /> <script id="Cookiebot" data-cfasync="false" src="https://consent.cookiebot.com/uc.js" data-cbid="51491ddd-fe7a-4425-ab39-69c78c55829f" type="text/javascript" async></script>  <script type="text/plain" data-cookieconsent="statistics"> (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f); 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<div class='html-font-label large-4 medium-4 small-12 columns'>Background:</div> <div class='large-8 medium-8 small-12 columns'> <div class="html-article-menu-option html-nav-bg html-nav-bright" data-bg="bright"> <i class="fa fa-file-text"></i> </div> <div class="html-article-menu-option html-nav-bg html-nav-dark" data-bg="dark"> <i class="fa fa-file-text-o"></i> </div> <div class="html-article-menu-option html-nav-bg html-nav-creme" data-bg="creme"> <i class="fa fa-file-text"></i> </div> </div> </div> </div> </div> </div> </div> <article ><div class='html-article-content'> <div style="margin-bottom: 10px;"> <strong>This is an early access version, the complete PDF, HTML, and XML versions will be available soon.</strong> </div> <span itemprop="publisher" content="Multidisciplinary Digital Publishing Institute"></span><span itemprop="url" content="https://www.mdpi.com/1424-8220/24/23/7590"></span> <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> <h1 class="title hypothesis_container" itemprop="name"> MGL-YOLO: A Lightweight Barcode Target Detection Algorithm </h1> <div class="art-authors hypothesis_container"> by <span class="inlineblock "><div class='profile-card-drop' data-dropdown='profile-card-drop13457542' data-options='is_hover:true, hover_timeout:5000'> Yuanhao Qu</div><div id="profile-card-drop13457542" 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">Yuanhao Qu</span></div></div></div><div class="profile-card__buttons" style="margin-bottom: 10px;"><a href="https://sciprofiles.com/profile/author/TEtmbDUvYkx0VDRxUHVRd2o5UzRBMExNY25Pd3psRGRUaFgvL3FQMllnST0=?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name" class="button button--color-inversed" target="_blank"> SciProfiles </a><a href="https://scilit.net/scholars?q=Yuanhao%20Qu" class="button button--color-inversed" target="_blank"> Scilit </a><a href="https://www.preprints.org/search?search1=Yuanhao%20Qu&field1=authors" class="button button--color-inversed" target="_blank"> Preprints.org </a><a href="https://scholar.google.com/scholar?q=Yuanhao%20Qu" class="button button--color-inversed" target="_blank" rels="noopener noreferrer"> Google Scholar </a></div><div class="profile-card__biography"><div class="js-profile-card__biography-item js-default-open"> YUANHAO QU was born in 2000 in Xuchang, Henan, China. He received his bachelor's degree in Design, a聽[...] </div><div class="js-profile-card__biography-item js-default-closed" style="display: none;"> YUANHAO QU was born in 2000 in Xuchang, Henan, China. He received his bachelor's degree in Mechanical Design, Manufacturing, and Automation from Henan University of Science and Technology in 2018. He is currently pursuing a master's degree in Mechanical Engineering at Henan University of Science and Technology. His current research interests include deep learning and computer vision. </div><div><a href="#" class='js-profile-card__biography-item js-default-open'>Read more</a><a href="#" class='js-profile-card__biography-item js-default-closed' style="display: none;">Read less</a></div></div></div><sup></sup><span style="display: inline; margin-left: 5px;"></span><a class="toEncode emailCaptcha visibility-hidden" data-author-id="13457542" href="/cdn-cgi/l/email-protection#b09fd3ded49dd3d7d99fdc9fd5ddd1d9dc9dc0c2dfc4d5d3c4d9dfde93808080d3878981888187818181888186808880d3838980d180d480d385878181818880d380d180d4858781d381d480d3858781d18187"><sup><i class="fa fa-envelope-o"></i></sup></a> and </span><span class="inlineblock "><div class='profile-card-drop' data-dropdown='profile-card-drop13457543' data-options='is_hover:true, hover_timeout:5000'> Fengshou Zhang</div><div id="profile-card-drop13457543" 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">Fengshou Zhang</span></div></div></div><div class="profile-card__buttons" style="margin-bottom: 10px;"><a href="https://sciprofiles.com/profile/2944514?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name" class="button button--color-inversed" target="_blank"> SciProfiles </a><a href="https://scilit.net/scholars?q=Fengshou%20Zhang" class="button button--color-inversed" target="_blank"> Scilit </a><a href="https://www.preprints.org/search?search1=Fengshou%20Zhang&field1=authors" class="button button--color-inversed" target="_blank"> Preprints.org </a><a href="https://scholar.google.com/scholar?q=Fengshou%20Zhang" class="button button--color-inversed" target="_blank" rels="noopener noreferrer"> Google Scholar </a></div><div class="profile-card__biography"><div class="js-profile-card__biography-item js-default-open"> FENGSHOU ZHANG received his Ph.D. in Aerospace Manufacturing Engineering from Northwestern in 2007.聽[...] </div><div class="js-profile-card__biography-item js-default-closed" style="display: none;"> FENGSHOU ZHANG received his Ph.D. in Aerospace Manufacturing Engineering from Northwestern Polytechnical University in 2007. He is currently a professor and Ph.D. advisor at the School of Mechanical and Electrical Engineering at Henan University of Science and Technology. In recent years, he has led and participated in three projects funded by the National Natural Science Foundation, three national key projects, and two provincial key technological projects. He has been granted over 11 patents and has published more than 70 papers. His research focuses on CNC technology, machine vision, precision measurement, and intelligent manufacturing. </div><div><a href="#" class='js-profile-card__biography-item js-default-open'>Read more</a><a href="#" class='js-profile-card__biography-item js-default-closed' style="display: none;">Read less</a></div></div></div><sup> *</sup><span style="display: inline; margin-left: 5px;"></span><a class="toEncode emailCaptcha visibility-hidden" data-author-id="13457543" href="/cdn-cgi/l/email-protection#a18ec2cfc58cc2c6c88ecd8ec4ccc0c8cd8cd1d3ced5c4c2d5c8cecf8291919192979791999190909491c49198909290c291c4919691999190939794969491949495999194919891c3"><sup><i class="fa fa-envelope-o"></i></sup></a></span> </div> <div class="nrm"></div> <span style="display:block; height:6px;"></span> <div></div> <div style="margin: 5px 0 15px 0;" class="hypothesis_container"> <div class="art-affiliations"> <div class="affiliation "> <div class="affiliation-name ">School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China</div> </div> <div class="affiliation"> <div class="affiliation-item"><sup>*</sup></div> <div class="affiliation-name ">Author to whom correspondence should be addressed. </div> </div> </div> </div> <div class="bib-identity" style="margin-bottom: 10px;"> <em>Sensors</em> <b>2024</b>, <em>24</em>(23), 7590; <a href="https://doi.org/10.3390/s24237590">https://doi.org/10.3390/s24237590</a> </div> <div class="pubhistory" style="font-weight: bold; padding-bottom: 10px;"> <span style="display: inline-block">Submission received: 1 October 2024</span> / <span style="display: inline-block">Revised: 14 November 2024</span> / <span style="display: inline-block">Accepted: 24 November 2024</span> / <span style="display: inline-block">Published: 27 November 2024</span> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/sensors/sections/sensing_imaging">Sensing and Imaging</a>)<br/> </div> <div class="highlight-box1"> <div class="download"> <a class="button button--color-inversed button--drop-down" data-dropdown="drop-download-1531347" aria-controls="drop-supplementary-1531347" aria-expanded="false"> Download <i class="material-icons">keyboard_arrow_down</i> </a> <div id="drop-download-1531347" class="f-dropdown label__btn__dropdown label__btn__dropdown--button" data-dropdown-content aria-hidden="true" tabindex="-1"> <a class="UD_ArticlePDF" href="/1424-8220/24/23/7590/pdf?version=1732723427" data-name="MGL-YOLO: A Lightweight Barcode Target Detection Algorithm" data-journal="sensors">Download PDF</a> <br/> </div> <a class="button button--color-inversed" href="/1424-8220/24/23/7590/notes">Versions Notes</a> </div> </div> <div class="responsive-moving-container small hidden" data-id="article-counters" style="margin-top: 15px;"></div> <div class="html-dynamic"> <section> <h2>Abstract</h2> <div class="art-abstract art-abstract-new in-tab hypothesis_container"> Due to the critical importance of one-dimensional barcode detection in logistics, retail, and manufacturing, which has become a key issue affecting operational efficiency, researchers have shown increasing interest in this area. However, deploying deep convolutional neural networks on embedded and some edge devices is very challenging due to limited storage space and computational resources. To address this issue, this paper proposes MGL-YOLO, a lightweight one-dimensional barcode detection network based on an improved YOLOv8, which aims to achieve a high detection accuracy at low computational cost. First, a new multi-scale group convolution (MSGConv) is designed and integrated into the C2f module to construct the MSG-C2f feature extraction module. By replacing the C2f module in the P5 layer of the backbone network, the ability to extract multi-scale feature information is enhanced. Secondly, a feature extraction module, Group RepConv Cross Stage Partial Efficient Long-Range Attention Network (GRCE), is designed to optimize the feature extraction capability of the C2f modules in the neck section, offering significant advantages in multi-scale characteristics and complexity adjustment. Finally, a Lightweight Shared Multi-Scale Detection Head (LSMD) is proposed, which improves the model’s detection accuracy and adaptability while reducing the model’s parameter size and computational complexity. Experimental results show that the proposed algorithm increases MAP50 and MAP50.95 by 2.57% and 2.31%, respectively, compared to YOLOv8, while reducing parameter size and computational cost by 36.21% and 34.15%, respectively. Moreover, it also demonstrates advantages in average precision compared to other object detection networks, proving the effectiveness of MGL-YOLO for one-dimensional barcode detection in complex backgrounds. </div> <div class="art-keywords in-tab hypothesis_container"><strong>Keywords: </strong> <span itemprop="keywords" style="display:none">one-dimensional barcode recognition; target detection; lightweight network; feature extraction; deep learning</span> <span> <a href="/search?q=one-dimensional%20barcode%20recognition">one-dimensional barcode recognition</a>; <a href="/search?q=target%20detection">target detection</a>; <a href="/search?q=lightweight%20network">lightweight network</a>; <a href="/search?q=feature%20extraction">feature extraction</a>; <a href="/search?q=deep%20learning">deep learning</a></span> </div> </section> </div> <div class="additional-content"> <h2><a name="cite"></a>Share and Cite</h2> <div class="social-media-links" style="text-align: left;"> <a 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