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<!DOCTYPE html> <html > <head> <meta charset="utf-8"> <meta rel="search" type="application/opensearchdescription+xml" href="/open_search.xml" title="Academia.edu"> <meta content="width=device-width, initial-scale=1" name="viewport"> <meta name="google-site-verification" content="bKJMBZA7E43xhDOopFZkssMMkBRjvYERV-NaN4R6mrs"> <meta name="csrf-param" content="authenticity_token" /> <meta name="csrf-token" content="MJiI6tRogUT71d0wNw3NYAyc/Srr4Rc+OTc7VgwfGpXrx1N2ZOORdLQDhH/dvZxKLAaq6gql8PSYClOgu+RimA==" /> <meta name="citation_title" content="Comparing the Performance of Pre-trained Deep Learning Models in Object Detection and Recognition" /> <meta name="citation_publication_date" content="2022/01/01" /> <meta name="citation_journal_title" content="Journal of Information Technology Management (JITM)" /> <meta name="citation_author" content="Journal of Information Technology Management" /> <meta name="twitter:card" content="summary" /> <meta name="twitter:url" content="https://www.academia.edu/97160040/Comparing_the_Performance_of_Pre_trained_Deep_Learning_Models_in_Object_Detection_and_Recognition" /> <meta name="twitter:title" content="Comparing the Performance of Pre-trained Deep Learning Models in Object Detection and Recognition" /> <meta name="twitter:description" content="The aim of this study is to evaluate the performance of the pre-trained models and compare them with the probability percentage of prediction in terms of execution time. This study uses the COCO dataset to evaluate both pre-trained image recognition" /> <meta name="twitter:image" content="https://0.academia-photos.com/61662134/16020519/26690960/s200_information.technology_management.jpg" /> <meta property="fb:app_id" content="2369844204" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://www.academia.edu/97160040/Comparing_the_Performance_of_Pre_trained_Deep_Learning_Models_in_Object_Detection_and_Recognition" /> <meta property="og:title" content="Comparing the Performance of Pre-trained Deep Learning Models in Object Detection and Recognition" /> <meta property="og:image" content="http://a.academia-assets.com/images/open-graph-icons/fb-paper.gif" /> <meta property="og:description" content="The aim of this study is to evaluate the performance of the pre-trained models and compare them with the probability percentage of prediction in terms of execution time. This study uses the COCO dataset to evaluate both pre-trained image recognition" /> <meta property="article:author" content="https://independent.academia.edu/InformationTechnologyManagement" /> <meta name="description" content="The aim of this study is to evaluate the performance of the pre-trained models and compare them with the probability percentage of prediction in terms of execution time. This study uses the COCO dataset to evaluate both pre-trained image recognition" /> <title>(PDF) Comparing the Performance of Pre-trained Deep Learning Models in Object Detection and Recognition | Journal of Information Technology Management - Academia.edu</title> <link rel="canonical" href="https://www.academia.edu/97160040/Comparing_the_Performance_of_Pre_trained_Deep_Learning_Models_in_Object_Detection_and_Recognition" /> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "single_work", 'action': "show", 'controller_action': 'single_work#show', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script> var $controller_name = 'single_work'; var $action_name = "show"; var $rails_env = 'production'; var $app_rev = '49879c2402910372f4abc62630a427bbe033d190'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.require = { config: function() { return function() {} } } </script> <script> window.Aedu = window.Aedu || {}; window.Aedu.hit_data = null; window.Aedu.serverRenderTime = new Date(1732452673000); window.Aedu.timeDifference = new Date().getTime() - 1732452673000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"The aim of this study is to evaluate the performance of the pre-trained models and compare them with the probability percentage of prediction in terms of execution time. This study uses the COCO dataset to evaluate both pre-trained image recognition and object detection, models. The results revealed that Tiny-YoloV3 is considered the best method for real-time applications as it takes less time. Whereas ResNet 50 is required for those applications which require a high probability percentage of prediction, such as medical image classification. In general, the rate of probability varies from 75% to 90% for the large objects in ResNet 50. Whereas in Tiny-YoloV3, the rate varies from 35% to 80% for large objects, besides it","author":[{"@context":"https://schema.org","@type":"Person","name":"Journal of Information Technology Management"}],"contributor":[],"dateCreated":"2023-02-19","dateModified":null,"datePublished":"2022-01-01","headline":"Comparing the Performance of Pre-trained Deep Learning Models in Object Detection and Recognition","inLanguage":"en","keywords":["Deep Learning"],"locationCreated":null,"publication":"Journal of Information Technology Management (JITM)","publisher":{"@context":"https://schema.org","@type":"Organization","name":null},"image":null,"thumbnailUrl":null,"url":"https://www.academia.edu/97160040/Comparing_the_Performance_of_Pre_trained_Deep_Learning_Models_in_Object_Detection_and_Recognition","sourceOrganization":[{"@context":"https://schema.org","@type":"EducationalOrganization","name":null}]}</script><link rel="stylesheet" media="all" 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evaluate the performance of the pre-trained models and compare them with the probability percentage of prediction in terms of execution time. This study uses the COCO dataset to evaluate both pre-trained image recognition and object detection, models. The results revealed that Tiny-YoloV3 is considered the best method for real-time applications as it takes less time. Whereas ResNet 50 is required for those applications which require a high probability percentage of prediction, such as medical image classification. In general, the rate of probability varies from 75% to 90% for the large objects in ResNet 50. Whereas in Tiny-YoloV3, the rate varies from 35% to 80% for large objects, besides it","publication_date":"2022,,","publication_name":"Journal of Information Technology Management (JITM)"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Comparing the Performance of Pre-trained Deep Learning Models in Object Detection and Recognition","broadcastable":false,"draft":false,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [61662134]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "control"; 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":98858625,"attachmentType":"pdf"}"><img alt="First page of “Comparing the Performance of Pre-trained Deep Learning Models in Object Detection and Recognition”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/98858625/mini_magick20230219-1-wl6baw.png?1676815014" /><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">Comparing the Performance of Pre-trained Deep Learning Models in Object Detection and Recognition</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="61662134" href="https://independent.academia.edu/InformationTechnologyManagement"><img alt="Profile image of Journal of Information Technology Management" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/61662134/16020519/26690960/s65_information.technology_management.jpg" />Journal of Information Technology Management</a></div><p class="ds-work-card--detail ds2-5-body-sm">2022, Journal of Information Technology Management (JITM)</p><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">The aim of this study is to evaluate the performance of the pre-trained models and compare them with the probability percentage of prediction in terms of execution time. This study uses the COCO dataset to evaluate both pre-trained image recognition and object detection, models. The results revealed that Tiny-YoloV3 is considered the best method for real-time applications as it takes less time. Whereas ResNet 50 is required for those applications which require a high probability percentage of prediction, such as medical image classification. In general, the rate of probability varies from 75% to 90% for the large objects in ResNet 50. Whereas in Tiny-YoloV3, the rate varies from 35% to 80% for large objects, besides it</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":98858625,"attachmentType":"pdf","workUrl":"https://www.academia.edu/97160040/Comparing_the_Performance_of_Pre_trained_Deep_Learning_Models_in_Object_Detection_and_Recognition"}">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":98858625,"attachmentType":"pdf","workUrl":"https://www.academia.edu/97160040/Comparing_the_Performance_of_Pre_trained_Deep_Learning_Models_in_Object_Detection_and_Recognition"}"><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" data-client_id="331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b" data-doc_id="98858625" data-landing_url="https://www.academia.edu/97160040/Comparing_the_Performance_of_Pre_trained_Deep_Learning_Models_in_Object_Detection_and_Recognition" data-login_uri="https://www.academia.edu/registrations/google_one_tap" data-moment_callback="onGoogleOneTapEvent" id="g_id_onload"></div><div class="ds-top-related-works--grid-container"><div class="ds-related-content--container ds-top-related-works--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="0" data-entity-id="104767289" 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/104767289/Evaluation_of_Pre_Trained_Convolutional_Neural_Network_Models_for_Object_Recognition">Evaluation of Pre-Trained Convolutional Neural Network Models for Object Recognition</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="234248248" href="https://independent.academia.edu/NadiaFazira5">Nadia Fazira</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Engineering &amp; Technology, 2018</p><p class="ds-related-work--abstract ds2-5-body-sm">This paper aims to evaluate the accuracy performance of pre-trained Convolutional Neural Network (CNN) models, namely AlexNet and GoogLeNet accompanied by one custom CNN. 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ds2-5-body-link" data-author-id="215359403" href="https://independent.academia.edu/AmitVishvasDivekar19BAI1002">Amit Vishvas Divekar 19BAI1002</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2021</p><p class="ds-related-work--abstract ds2-5-body-sm">A computer views all kinds of visual media as an array of numerical values. 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Recently object recognition has been dominated by the use of Deep Neural Networks (DNN), however, they required a large number of images and long training times. If a user asks a service robot to search for an unknown object, it has to deal with selecting relevant images to learn a model, deal with polysemy, and learn a model relatively quickly to be of any use to the user. In this paper we describe an object recognition system that deals with the above challenges by: (i) a user interface to reduce different object interpretations, (ii) downloading on-the-fly images from Internet to train a model, and (iii) using the outputs of a trimmed pre-trained DNN as attributes for a SVM. The whole process (selecting and downloading images and training a model) of learning a model for an unknown object takes around two minutes. The proposed method was tested on 72 common objects found in a house enviro...</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":"Fast Learning for Accurate Object Recognition Using a Pre-trained Deep Neural Network","attachmentId":104536749,"attachmentType":"pdf","work_url":"https://www.academia.edu/104945323/Fast_Learning_for_Accurate_Object_Recognition_Using_a_Pre_trained_Deep_Neural_Network","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/104945323/Fast_Learning_for_Accurate_Object_Recognition_Using_a_Pre_trained_Deep_Neural_Network"><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="43639234" 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/43639234/Performance_evaluation_of_fine_tuned_faster_R_CNN_on_specific_MS_COCO_objects">Performance evaluation of fine-tuned faster R-CNN on specific MS COCO objects</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="164602726" href="https://independent.academia.edu/GarimaDevnani">Garima Devnani</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="163474776" href="https://independent.academia.edu/JournalIJECE">International Journal of Electrical and Computer Engineering (IJECE)</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Electrical and Computer Engineering (IJECE), 2019</p><p class="ds-related-work--abstract ds2-5-body-sm">Fine-tuning of a model is a method that is most often required to cater the users' explicit requirements. But the question remains whether the model is accurate enough to be used for a certain application. This paper strives to present the metrics used for performance evaluation of a Convolutional Neural Network (CNN) model. The evaluation is based on the training process which provides us with intermediate models after every 1000 iterations. While 1000 iterations is not substantial enough over the range of 490k iterations, the groups are sized with 100k iterations each. Now, the intention was to compare the recorded metrics to evaluate the model in terms of accuracy. The training model used the set of specific categories chosen from the Microsoft Common Objects in Context (MS COCO) dataset, while allowing users to use their own externally available images to test the model's accuracy. Our trained model ensured that all the objects are detected that are present in the image to depict the effect of precision.</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":"Performance evaluation of fine-tuned faster R-CNN on specific MS COCO objects","attachmentId":63939668,"attachmentType":"pdf","work_url":"https://www.academia.edu/43639234/Performance_evaluation_of_fine_tuned_faster_R_CNN_on_specific_MS_COCO_objects","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/43639234/Performance_evaluation_of_fine_tuned_faster_R_CNN_on_specific_MS_COCO_objects"><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="105639906" 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/105639906/A_Deep_Learning_Based_Approach_for_Real_Time_Object_Detection_and_Recognition">A Deep Learning-Based Approach for Real-Time Object Detection and Recognition</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="144188512" href="https://independent.academia.edu/AmitJuyal1">Amit Juyal</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Mathematical Statistician and Engineering Applications</p><p class="ds-related-work--abstract ds2-5-body-sm">Object detection and recognition is an essential task in computer vision with numerous real-world applications such as surveillance, self-driving cars, and robotics. In recent years, deep learning-based approaches have significantly improved the accuracy and speed of object detection and recognition. The You Only Look Once version 3 (YOLOv3) algorithm is a popular deep learning-based approach that can detect and recognize objects in real-time. The Common Objects in Context (COCO) dataset is a large-scale dataset with over 330,000 labeled images and more than 2.5 million object instances, making it a popular choice for object detection and recognition tasks. In this paper, we propose a deep learning-based approach for real-time object detection and recognition using the YOLOv3 architecture and COCO dataset. We evaluate our approach based on several performance metrics, including mean average precision (mAP), frames per second (FPS), total object detection time, object detection accur...</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 Deep Learning-Based Approach for Real-Time Object Detection and Recognition","attachmentId":105040508,"attachmentType":"pdf","work_url":"https://www.academia.edu/105639906/A_Deep_Learning_Based_Approach_for_Real_Time_Object_Detection_and_Recognition","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/105639906/A_Deep_Learning_Based_Approach_for_Real_Time_Object_Detection_and_Recognition"><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="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="9" 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":75916085,"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></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":98858625,"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":98858625,"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_98858625" 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. You can download the paper by clicking the button above.</p></div></div></div></div><div class="ds-sidebar--container js-work-sidebar"><div class="ds-related-content--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="0" data-entity-id="83350071" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/83350071/Object_Detection_using_Deep_Learning_Approach">Object Detection using Deep Learning Approach</a><div class="ds-related-work--metadata"><a class="js-related-work-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><div 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