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As a consequence of this approach, they require image processing algorithms to inspect contents of images. This project compares 3 major image processing algorithms: Single" /> <meta name="twitter:image" content="https://gravatar.com/avatar/6a1bebbb54012e47c62195c7e4d393b7?s=200" /> <meta property="fb:app_id" content="2369844204" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://www.academia.edu/80065271/Comparative_analysis_of_deep_learning_image_detection_algorithms" /> <meta property="og:title" content="Comparative analysis of deep learning image detection algorithms" /> <meta property="og:image" content="http://a.academia-assets.com/images/open-graph-icons/fb-paper.gif" /> <meta property="og:description" content="A computer views all kinds of visual media as an array of numerical values. As a consequence of this approach, they require image processing algorithms to inspect contents of images. 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As a consequence of this approach, they require image processing algorithms to inspect contents of images. This project compares 3 major image processing algorithms: Single Shot Detection (SSD), Faster Region based Convolutional Neural Networks (Faster R-CNN), and You Only Look Once (YOLO) to find the fastest and most efficient of three. In this comparative analysis, using the Microsoft COCO (Common Object in Context) dataset, the performance of these three algorithms is evaluated and their strengths and limitations are analysed based on parameters such as accuracy, precision and F1 score. From the results of the analysis, it can be concluded that the suitability of any of the algorithms over the other two is dictated to a great extent by the use cases they are applied in. In an identical testing environment, YOLO-v3 outperforms SSD and Faster R-CNN, making it the best of the three algorithms.","author":[{"@context":"https://schema.org","@type":"Person","name":"Ved Kulkarni"}],"contributor":[],"dateCreated":"2022-05-27","dateModified":"2022-05-27","datePublished":"2021-01-01","headline":"Comparative analysis of deep learning image detection algorithms","inLanguage":"en","keywords":["Computer Science","Artificial Intelligence","Deep Learning","Big Data"],"locationCreated":null,"publication":"Journal of Big Data","publisher":{"@context":"https://schema.org","@type":"Organization","name":"Springer Science and Business Media LLC"},"image":null,"thumbnailUrl":null,"url":"https://www.academia.edu/80065271/Comparative_analysis_of_deep_learning_image_detection_algorithms","sourceOrganization":[{"@context":"https://schema.org","@type":"EducationalOrganization","name":null}]}</script><link rel="stylesheet" media="all" 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As a consequence of this approach, they require image processing algorithms to inspect contents of images. This project compares 3 major image processing algorithms: Single Shot Detection (SSD), Faster Region based Convolutional Neural Networks (Faster R-CNN), and You Only Look Once (YOLO) to find the fastest and most efficient of three. In this comparative analysis, using the Microsoft COCO (Common Object in Context) dataset, the performance of these three algorithms is evaluated and their strengths and limitations are analysed based on parameters such as accuracy, precision and F1 score. From the results of the analysis, it can be concluded that the suitability of any of the algorithms over the other two is dictated to a great extent by the use cases they are applied in. In an identical testing environment, YOLO-v3 outperforms SSD and Faster R-CNN, making it the best of the three algorithms.","publisher":"Springer Science and Business Media LLC","publication_date":"2021,,","publication_name":"Journal of Big Data"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Comparative analysis of deep learning image detection algorithms","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [215016473]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; window.loswp.appleClientId = 'edu.academia.applesignon';</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":86570965,"attachmentType":"pdf"}"><img alt="First page of “Comparative analysis of deep learning image detection algorithms”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/86570965/mini_magick20220527-10038-nmyq8k.png?1653676737" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/assets/single_work_splash/adobe.icon-574afd46eb6b03a77a153a647fb47e30546f9215c0ee6a25df597a779717f9ef.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">Comparative analysis of deep learning image detection algorithms</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="215016473" href="https://independent.academia.edu/VedKulkarni5"><img alt="Profile image of Ved Kulkarni" class="ds-work-card--author-avatar" src="https://gravatar.com/avatar/6a1bebbb54012e47c62195c7e4d393b7?s=65" />Ved Kulkarni</a></div><p class="ds-work-card--detail ds2-5-body-sm">2021, Journal of Big Data</p><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">A computer views all kinds of visual media as an array of numerical values. As a consequence of this approach, they require image processing algorithms to inspect contents of images. This project compares 3 major image processing algorithms: Single Shot Detection (SSD), Faster Region based Convolutional Neural Networks (Faster R-CNN), and You Only Look Once (YOLO) to find the fastest and most efficient of three. In this comparative analysis, using the Microsoft COCO (Common Object in Context) dataset, the performance of these three algorithms is evaluated and their strengths and limitations are analysed based on parameters such as accuracy, precision and F1 score. From the results of the analysis, it can be concluded that the suitability of any of the algorithms over the other two is dictated to a great extent by the use cases they are applied in. In an identical testing environment, YOLO-v3 outperforms SSD and Faster R-CNN, making it the best of the three algorithms.</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":86570965,"attachmentType":"pdf","workUrl":"https://www.academia.edu/80065271/Comparative_analysis_of_deep_learning_image_detection_algorithms"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":86570965,"attachmentType":"pdf","workUrl":"https://www.academia.edu/80065271/Comparative_analysis_of_deep_learning_image_detection_algorithms"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div></div><div data-auto_select="false" 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There is a tremendous scope in the applications that would benefit with more innovations in the domain of object detection. Rapid growth in the field of machine learning has complemented the efforts in this area and in the recent times, research community has contributed a lot in real time object detection. In the current work, authors have implemented real time object detection and have made efforts to improve the accuracy of the detection mechanism. In the current research, we have used ssd_v2_inception_coco model as Single Shot Detection models deliver significantly better results. A dataset of more than 100 raw images is used for training and then xml files are generated using labellimg. Tensor flow records generated are passed through training pipelines using the proposed model. OpenCV captures real-time images and CNN performs con...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Real Time Object Detection using CNN based Single Shot Detector Model","attachmentId":75808167,"attachmentType":"pdf","work_url":"https://www.academia.edu/63348907/Real_Time_Object_Detection_using_CNN_based_Single_Shot_Detector_Model","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/63348907/Real_Time_Object_Detection_using_CNN_based_Single_Shot_Detector_Model"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="108017557" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/108017557/Object_Detection_System_Based_on_Convolution_Neural_Networks_Using_Single_Shot_Multi_Box_Detector">Object Detection System Based on Convolution Neural Networks Using Single Shot Multi-Box Detector</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="177443884" href="https://independent.academia.edu/SonamSrivastava12">Sonam Srivastava</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Procedia Computer Science, 2020</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Object Detection System Based on Convolution Neural Networks Using Single Shot Multi-Box Detector","attachmentId":106516328,"attachmentType":"pdf","work_url":"https://www.academia.edu/108017557/Object_Detection_System_Based_on_Convolution_Neural_Networks_Using_Single_Shot_Multi_Box_Detector","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/108017557/Object_Detection_System_Based_on_Convolution_Neural_Networks_Using_Single_Shot_Multi_Box_Detector"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="92926583" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/92926583/Object_Detection_and_Identification_Using_Deep_Learning_and_OpenCV">Object Detection and Identification Using Deep Learning and OpenCV</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="6079060" href="https://independent.academia.edu/IJRASETPublication">IJRASET Publication</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022</p><p class="ds-related-work--abstract ds2-5-body-sm">In recent years, deep learning has had a significant impact on "how the world is adjusting to artificial intelligence". Region-based Convolutional Neural Networks (RCNN), Faster R-CNN, Single Shot Detector (SSD), and You Only Look Once (YOLO) are a few of the well-known object identification techniques. When speed is prioritized above accuracy, YOLO outperforms others, with Faster-RCNN and SSD having greater accuracy. In order to execute detection and tracking efficiently, deep learning blends SSD and Mobile Nets. This method detects objects effectively without sacrificing speed.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Object Detection and Identification Using Deep Learning and OpenCV","attachmentId":95807178,"attachmentType":"pdf","work_url":"https://www.academia.edu/92926583/Object_Detection_and_Identification_Using_Deep_Learning_and_OpenCV","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/92926583/Object_Detection_and_Identification_Using_Deep_Learning_and_OpenCV"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="113119860" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/113119860/Statistical_Analysis_of_Design_Aspects_of_Various_YOLO_Based_Deep_Learning_Models_for_Object_Detection">Statistical Analysis of Design Aspects of Various YOLO-Based Deep Learning Models for Object Detection</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="188463664" href="https://independent.academia.edu/UddagiriSirisha20PHD7077">Uddagiri Sirisha 20PHD7077</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Computational Intelligence Systems, 2023</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Statistical Analysis of Design Aspects of Various YOLO-Based Deep Learning Models for Object Detection","attachmentId":110163857,"attachmentType":"pdf","work_url":"https://www.academia.edu/113119860/Statistical_Analysis_of_Design_Aspects_of_Various_YOLO_Based_Deep_Learning_Models_for_Object_Detection","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/113119860/Statistical_Analysis_of_Design_Aspects_of_Various_YOLO_Based_Deep_Learning_Models_for_Object_Detection"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="111374144" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/111374144/An_improved_deep_learning_based_optimal_object_detection_system_from_images">An improved deep learning-based optimal object detection system from images</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="162876997" href="https://independent.academia.edu/preetirani27">preeti rani</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Multimedia Tools and Applications</p><p class="ds-related-work--abstract ds2-5-body-sm">Computer vision technology for detecting objects in a complex environment often includes other key technologies, including pattern recognition, artificial intelligence, and digital image processing. It has been shown that Fast Convolutional Neural Networks (CNNs) with You Only Look Once (YOLO) is optimal for differentiating similar objects, constant motion, and low image quality. The proposed study aims to resolve these issues by implementing three different object detection algorithms—You Only Look Once (YOLO), Single Stage Detector (SSD), and Faster Region-Based Convolutional Neural Networks (R-CNN). This paper compares three different deep-learning object detection methods to find the best possible combination of feature and accuracy. The R-CNN object detection techniques are performed better than single-stage detectors like Yolo (You Only Look Once) and Single Shot Detector (SSD) in term of accuracy, recall, precision and loss.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"An improved deep learning-based optimal object detection system from images","attachmentId":108933569,"attachmentType":"pdf","work_url":"https://www.academia.edu/111374144/An_improved_deep_learning_based_optimal_object_detection_system_from_images","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/111374144/An_improved_deep_learning_based_optimal_object_detection_system_from_images"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":86570965,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":86570965,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_86570965" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. 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