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(PDF) IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 1 Object Detection With Deep Learning: A Review
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window.loswp.showSignupCaptcha = false window.loswp.willEdgeCache = false; window.loswp.work = {"work":{"id":42798727,"created_at":"2020-04-20T10:04:55.079-07:00","from_world_paper_id":null,"updated_at":"2024-11-14T20:59:58.817-08:00","_data":{"grobid_abstract":"Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles that combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy, and optimization function. In this paper, we provide a review of deep learning-based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely, the convolutional neural network. Then, we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection, and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network-based learning systems. in 2010, where he is currently pursuing the Ph.D.","grobid_abstract_attachment_id":"63026559"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 1 Object Detection With Deep Learning: A Review","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [154860094]; 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.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</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":63026559,"attachmentType":"pdf"}"><img alt="First page of “IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 1 Object Detection With Deep Learning: A Review”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/63026559/mini_magick20200420-20383-or8qn3.png?1587402627" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.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">IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 1 Object Detection With Deep Learning: A Review</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="154860094" href="https://vnuhcm.academia.edu/V%C3%B5Ho%C3%A0ngTh%E1%BB%A7yTi%C3%AAn"><img alt="Profile image of Võ Hoàng Thủy Tiên" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/154860094/49474791/37451161/s65_v_ho_ng.th_y_ti_n.png" />Võ Hoàng Thủy Tiên</a></div><div class="ds-work-card--detail"><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">21 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 42798727; 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if (!viewCountBody) { throw new Error('Failed to find work views element'); } viewCountBody.textContent = `${commaizedViewCount} views`; } catch (error) { // Remove the whole views element if there was some issue parsing. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); throw new Error(`Failed to parse view count: ${viewCount}`, error); } }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles that combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy, and optimization function. In this paper, we provide a review of deep learning-based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely, the convolutional neural network. Then, we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection, and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network-based learning systems. in 2010, where he is currently pursuing the Ph.D.</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":63026559,"attachmentType":"pdf","workUrl":"https://www.academia.edu/42798727/IEEE_TRANSACTIONS_ON_NEURAL_NETWORKS_AND_LEARNING_SYSTEMS_1_Object_Detection_With_Deep_Learning_A_Review"}">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":63026559,"attachmentType":"pdf","workUrl":"https://www.academia.edu/42798727/IEEE_TRANSACTIONS_ON_NEURAL_NETWORKS_AND_LEARNING_SYSTEMS_1_Object_Detection_With_Deep_Learning_A_Review"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div><div class="ds-signup-banner-trigger-container"><div class="ds-signup-banner-trigger ds-signup-banner-trigger-control"></div></div><div class="ds-signup-banner ds-signup-banner-control"><div id="ds-signup-banner-close-button"><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--inverse"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">close</span></button></div><div class="ds-signup-banner-ctas"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><h4 class="ds2-5-heading-serif-sm">Sign up for access to the world's latest research</h4><button class="ds2-5-button ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"signup-banner"}">Sign up for free<span class="material-symbols-outlined" style="font-size: 20px" translate="no">arrow_forward</span></button></div><div class="ds-signup-banner-divider"></div><div class="ds-signup-banner-reasons"><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Get notified about relevant papers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Save papers to use in your research</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Join the discussion with peers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Track your impact</span></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. 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Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles which combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy and optimization function, etc. In this paper, we provide a review on deep learning based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely Convolutional Neural Network (CNN). Then we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network based learning systems.</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 with Deep Learning: A Review","attachmentId":61773718,"attachmentType":"pdf","work_url":"https://www.academia.edu/41617707/Object_Detection_with_Deep_Learning_A_Review","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/41617707/Object_Detection_with_Deep_Learning_A_Review"><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="1" data-entity-id="63506075" 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/63506075/An_Overview_of_Deep_Learning_Based_Object_Detection_Methods">An Overview of Deep Learning-Based Object Detection Methods</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="136502108" href="https://univbatna.academia.edu/LarbiGuezouli">Larbi Guezouli</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2018</p><p class="ds-related-work--abstract ds2-5-body-sm">In recent years, there has been rapid development in the research area of deep learning. Deep learning was used to solve different problems, such as visual recognition, speech recognition and handwriting recognition and was achieved a very good performance. In deep learning, Convolutional Neural Networks (ConvNets or CNNs) are found to give the most accurate results, in solving object detection problems. In this paper we&#39;ll go into summarizing some of the most important deep learning models used for object detection tasks over this last recent year, since the creation of AlexNet in 2012. Then, we&#39;ll make a comparison in terms of speed and accuracy between the most used state-of-the-art methods in object detection. Keywords— Object Detection, Deep Learning Methods, Convolutional Neural Networks</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 Overview of Deep Learning-Based Object Detection Methods","attachmentId":75916086,"attachmentType":"pdf","work_url":"https://www.academia.edu/63506075/An_Overview_of_Deep_Learning_Based_Object_Detection_Methods","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/63506075/An_Overview_of_Deep_Learning_Based_Object_Detection_Methods"><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="2" data-entity-id="67763862" 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/67763862/Object_Detection_Using_Deep_Learning">Object Detection Using Deep Learning</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="194931094" href="https://independent.academia.edu/MukulBhardwaj29">Mukul Bhardwaj</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2020</p><p class="ds-related-work--abstract ds2-5-body-sm">It has received much research attention in recent years because of the close relationship between object detection and video analysis and image comprehension. Traditional methods of object detection are based on hand-crafted features and architectures that are flawlessly trainable. Their performance stagnates easily by constructing complex ensembles that combine multiple lowlevel image features with high-level context from object detectors and scene classifiers. With the rapid growth of deep learning, more efficient techniques are implemented to solve the problems inherent in conventional architectures, capable of learning semantic, high-level, and deeper features. These models act differently in the context of network design, training strategy, and optimization.</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 Using Deep Learning","attachmentId":78473879,"attachmentType":"pdf","work_url":"https://www.academia.edu/67763862/Object_Detection_Using_Deep_Learning","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/67763862/Object_Detection_Using_Deep_Learning"><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="3" data-entity-id="78855025" 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/78855025/Object_Detection_Techniques_based_on_Deep_Learning_A_Review">Object Detection Techniques based on Deep Learning: A Review</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="145843887" href="https://uitrgpv.academia.edu/UtkarshNamdev">Utkarsh Namdev</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Computer Science &amp; Engineering: An International Journal, 2022</p><p class="ds-related-work--abstract ds2-5-body-sm">Object detection is a computer technique that searches digital images and videos for occurrences of meaningful subjects in particular categories (such as people, buildings, and automobiles). It is related to computer vision and image processing. Two well-studied aspects of identification are facial and pedestrian detection. Object detection is useful in a wide range of visual recognition tasks, including image retrieval and video monitoring. The object detection algorithm has been improved many times to improve the performance in terms of speed and accuracy. “Due to the tireless efforts of many researchers, deep learning algorithms are rapidly improving their object detection performance. Pedestrian detection, medical imaging, robotics, self-driving cars, face recognition and other popular applications have reduced labor in many areas.” It is used in a wide variety of industries, with applications range from individual safeguarding to business productivity. It is a fundamental compo...</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 Techniques based on Deep Learning: A Review","attachmentId":85751803,"attachmentType":"pdf","work_url":"https://www.academia.edu/78855025/Object_Detection_Techniques_based_on_Deep_Learning_A_Review","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/78855025/Object_Detection_Techniques_based_on_Deep_Learning_A_Review"><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="4" data-entity-id="44229961" 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/44229961/A_Review_on_Object_Detection_Techniques_using_Deep_Learning">A Review on Object Detection Techniques using Deep Learning</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="31493941" href="https://irjet.academia.edu/IRJET">IRJET Journal</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Object Detection is one of the most challenging problems in computer vision. It attempts to find object instances in given images from a large number of categories that are already defined. Deep learning methods have developed to learn feature representations from data and have led to remarkable inventions in the field of object detection. As deep learning has observed rapid growth, more robust tools, which can learn semantic, high-level, deeper features, are proposed to address the issues existing in conventional architectures. Our review starts with an introduction of deep learning algorithms in the family Convolutional Neural Network (CNN). Then we focus on basic image segmentation techniques.</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":"A Review on Object Detection Techniques using Deep Learning","attachmentId":64598991,"attachmentType":"pdf","work_url":"https://www.academia.edu/44229961/A_Review_on_Object_Detection_Techniques_using_Deep_Learning","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/44229961/A_Review_on_Object_Detection_Techniques_using_Deep_Learning"><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="5" data-entity-id="40683300" 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/40683300/A_Survey_of_Deep_Learning_based_Object_Detection">A Survey of Deep Learning-based 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="131887224" href="https://uwaterloo.academia.edu/suryam">surya m</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in peoples life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning networks for detection tasks, the performance of object detectors has been greatly improved. In order to understand the main development status of object detection pipeline, thoroughly and deeply, in this survey, we first analyze the methods of existing typical detection models and describe the benchmark datasets. Afterwards and primarily, we provide a comprehensive overview of a variety of object detection methods in a systematic manner, covering the one-stage and two-stage detectors. Moreover, we list the traditional and new applications. Some representative branches of object detection are analyzed as well. Finally, we discuss the architecture of exploiting these object detection methods to build an effective and efficient system and point out a set of development trends to better follow the state-of-the-art algorithms and further research.</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":"A Survey of Deep Learning-based Object Detection","attachmentId":60965751,"attachmentType":"pdf","work_url":"https://www.academia.edu/40683300/A_Survey_of_Deep_Learning_based_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/40683300/A_Survey_of_Deep_Learning_based_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="6" data-entity-id="78902367" 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/78902367/Investigations_of_Object_Detection_in_Images_Videos_Using_Various_Deep_Learning_Techniques_and_Embedded_Platforms_A_Comprehensive_Review">Investigations of Object Detection in Images/Videos Using Various Deep Learning Techniques and Embedded Platforms—A Comprehensive Review</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="219318706" href="https://independent.academia.edu/BalaramMurthyC">Balaram Murthy C</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Applied Sciences, 2020</p><p class="ds-related-work--abstract ds2-5-body-sm">In recent years there has been remarkable progress in one computer vision application area: object detection. One of the most challenging and fundamental problems in object detection is locating a specific object from the multiple objects present in a scene. Earlier traditional detection methods were used for detecting the objects with the introduction of convolutional neural networks. From 2012 onward, deep learning-based techniques were used for feature extraction, and that led to remarkable breakthroughs in this area. This paper shows a detailed survey on recent advancements and achievements in object detection using various deep learning techniques. Several topics have been included, such as Viola–Jones (VJ), histogram of oriented gradient (HOG), one-shot and two-shot detectors, benchmark datasets, evaluation metrics, speed-up techniques, and current state-of-art object detectors. Detailed discussions on some important applications in object detection areas, including pedestrian...</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":"Investigations of Object Detection in Images/Videos Using Various Deep Learning Techniques and Embedded Platforms—A Comprehensive Review","attachmentId":85789560,"attachmentType":"pdf","work_url":"https://www.academia.edu/78902367/Investigations_of_Object_Detection_in_Images_Videos_Using_Various_Deep_Learning_Techniques_and_Embedded_Platforms_A_Comprehensive_Review","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/78902367/Investigations_of_Object_Detection_in_Images_Videos_Using_Various_Deep_Learning_Techniques_and_Embedded_Platforms_A_Comprehensive_Review"><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="67651300" 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/67651300/Exploring_Deep_Learning_Based_Architecture_Strategies_Applications_and_Current_Trends_in_Generic_Object_Detection_A_Comprehensive_Review">Exploring Deep Learning-Based Architecture, Strategies, Applications and Current Trends in Generic Object Detection: A Comprehensive Review</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="33189557" href="https://independent.academia.edu/EngrLubnaAziz">Engr.Lubna Aziz</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IEEE Access</p><p class="ds-related-work--abstract ds2-5-body-sm">Object detection is a fundamental but challenging issue in the field of generic image analysis; it plays an important role in a wide range of applications and has been receiving special attention in recent years. Although there are enomerous methods exist, an in-depth review of the literature concerning generic detection remains. This paper provides a comprehensive survey of recent advances in visual object detection with deep learning. Covering about 300 publications that we survey 1) region proposal-based object detection methods such as R-CNN, SPPnet, Fast R-CNN, Faster R-CNN, Mask RCN, RFCN, FPN, 2) classification/regression base object detection methods such as YOLO(v2 to v5), SSD, DSSD, RetinaNet, RefineDet, CornerNet, EfficientDet, M2Det 3) Some latest detectors such as, relation network for object detection, DCN v2, NAS FPN. Moreover, five publicly available benchmark datasets and their standard evaluation metrics are also discussed. We mainly focus on the application of deep learning architectures to five major applications, namely Object Detection in Surveillance, Military, Transportation, Medical, and Daily Life. In the survey, we cover a variety of factors affecting the detection performance in detail, such as i) a wide range of object categories and intra-class variations, ii) limited storage capacity and computational power. Finally, we finish the survey by identifying fifteen current trends and promising direction for future research. INDEX TERMS Object detection and recognition, deep learning, convolutional neural networks (CNN), and neural network.</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":"Exploring Deep Learning-Based Architecture, Strategies, Applications and Current Trends in Generic Object Detection: A Comprehensive Review","attachmentId":78392337,"attachmentType":"pdf","work_url":"https://www.academia.edu/67651300/Exploring_Deep_Learning_Based_Architecture_Strategies_Applications_and_Current_Trends_in_Generic_Object_Detection_A_Comprehensive_Review","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/67651300/Exploring_Deep_Learning_Based_Architecture_Strategies_Applications_and_Current_Trends_in_Generic_Object_Detection_A_Comprehensive_Review"><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="64673699" 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/64673699/Deep_Learning_Approaches_for_Object_Detection">Deep Learning Approaches 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="6266202" href="https://independent.academia.edu/sushmajaiswal">sushma jaiswal</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2020</p><p class="ds-related-work--abstract ds2-5-body-sm">In computer vision, object detection is a very important, exciting and mind-blowing study. Object detection work in numerous fields such as observing security, independently/autonomous driving and etc. Deep-learning based object detection techniques have developed at a very fast pace and have attracted the attention of many researchers. The main focus of the 21st century is the development of the object-detection framework, comprehensively and genuinely. In this investigation, we initially investigate and evaluate the various object detection approaches and designate the benchmark datasets. We also delivered the wide-ranging general idea of object detection approaches in an organized way. We covered the first and second stage detectors of object detection methods. And lastly, we consider the construction of these object detection approaches to give dimensions for further research.</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":"Deep Learning Approaches for Object Detection","attachmentId":76601168,"attachmentType":"pdf","work_url":"https://www.academia.edu/64673699/Deep_Learning_Approaches_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/64673699/Deep_Learning_Approaches_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="125474686" 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/125474686/Object_Detection_and_Classification_Through_Deep_Learning_Approaches">Object Detection and Classification Through Deep Learning Approaches</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="321190784" href="https://independent.academia.edu/DrSHRWANRAM">Dr. SHRWAN RAM</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of emerging technologies and innovative research, 2018</p><p class="ds-related-work--abstract ds2-5-body-sm">In this paper, we implemented the image classification and object detection. This paper presents a deep learning approach for traffic light detection in adapting a single shot detection(SSD) approach and image classification of two categories of bicycle by retraining inceptionv3 model both using an open source tool called TensorFlow Object Detection API. We reviewed the current literature on convolutional object detection and tested the implementability of one of the methods and discovered that convolutional object detection is still evolving as a technology despite that convolutional object detection has outranked other object detection methods. To implement object detection and image classification there is free availability of datasets and pretrained networks it is possible to create a functional implementation of a deep neural network without access to specialist hardware. KEYWORDS-Object detection, Deep learning, Convolutional neural network, TensorFlow Object Detection API, SS...</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 Classification Through Deep Learning Approaches","attachmentId":119510861,"attachmentType":"pdf","work_url":"https://www.academia.edu/125474686/Object_Detection_and_Classification_Through_Deep_Learning_Approaches","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/125474686/Object_Detection_and_Classification_Through_Deep_Learning_Approaches"><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":63026559,"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":63026559,"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_63026559" 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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