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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="9qQtBSOJ9LBJSn5S8LBZcYvt58EtNgB6vx25edZr3O-NYajYuE0dBKF4dOfuTVqn-eWCrn9bac4AHD31mtfsig" /> <meta name="citation_title" content="IRJET- Real Time Object Detection Using YOLOv3" /> <meta name="citation_publication_date" content="2020/01/01" /> <meta name="citation_journal_title" content="IRJET" /> <meta name="citation_author" content="IRJET Journal" /> <meta name="twitter:card" content="summary" /> <meta name="twitter:url" content="https://www.academia.edu/44168535/IRJET_Real_Time_Object_Detection_Using_YOLOv3" /> <meta name="twitter:title" content="IRJET- Real Time Object Detection Using YOLOv3" /> <meta name="twitter:description" content="Object detection using deep learning has achieved very good performance but there are many problems with images in real-world shooting such as noise, blurring or rotating jitter, etc. These problems have a great impact on object detection. 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The main" /> <title>(PDF) IRJET- Real Time Object Detection Using YOLOv3</title> <link rel="canonical" href="https://www.academia.edu/44168535/IRJET_Real_Time_Object_Detection_Using_YOLOv3" /> <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 = '075e914b9e16164113b5b9afd7238a56a7292942'; 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(1740050694000); window.Aedu.timeDifference = new Date().getTime() - 1740050694000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"Object detection using deep learning has achieved very good performance but there are many problems with images in real-world shooting such as noise, blurring or rotating jitter, etc. These problems have a great impact on object detection. The main objective is to detect objects using You Only Look Once (YOLO) approach. The YOLO method has several advantages as compared to other object detection algorithms. In other algorithms like Convolutional Neural Network (CNN), Fast-Convolutional Neural Network the algorithm will not look at the image completely, but in YOLO ,the algorithm looks the image completely by predicting the bounding boxes using convolutional network and finds class probabilities for these boxes and also detects the image faster as compared to other algorithms. We have used this algorithm for detecting different types of objects and have created an android application which would return voice feedback to the user.","author":[{"@context":"https://schema.org","@type":"Person","name":"IRJET Journal","url":"https://irjet.academia.edu/IRJET"}],"contributor":[],"dateCreated":"2020-09-26","datePublished":"2020-01-01","headline":"IRJET- Real Time Object Detection Using YOLOv3","image":"https://attachments.academia-assets.com/64527354/thumbnails/1.jpg","inLanguage":"en","keywords":["Mechanical Engineering"],"publication":"IRJET","publisher":{"@context":"https://schema.org","@type":"Organization","name":null},"sourceOrganization":[{"@context":"https://schema.org","@type":"EducationalOrganization","name":null}],"thumbnailUrl":"https://attachments.academia-assets.com/64527354/thumbnails/1.jpg","url":"https://www.academia.edu/44168535/IRJET_Real_Time_Object_Detection_Using_YOLOv3"}</script><style type="text/css">@media(max-width: 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{"work":{"id":44168535,"created_at":"2020-09-26T02:38:52.883-07:00","from_world_paper_id":null,"updated_at":"2024-11-26T12:58:30.558-08:00","_data":{"abstract":"Object detection using deep learning has achieved very good performance but there are many problems with images in real-world shooting such as noise, blurring or rotating jitter, etc. These problems have a great impact on object detection. The main objective is to detect objects using You Only Look Once (YOLO) approach. The YOLO method has several advantages as compared to other object detection algorithms. In other algorithms like Convolutional Neural Network (CNN), Fast-Convolutional Neural Network the algorithm will not look at the image completely, but in YOLO ,the algorithm looks the image completely by predicting the bounding boxes using convolutional network and finds class probabilities for these boxes and also detects the image faster as compared to other algorithms. We have used this algorithm for detecting different types of objects and have created an android application which would return voice feedback to the user.","ai_title_tag":"Real-Time YOLOv3 Object Detection with Voice Feedback App","publication_date":"2020,,","publication_name":"IRJET"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"IRJET- Real Time Object Detection Using YOLOv3","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [31493941]; 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.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 ds-work-card--no-bottom-spacing"><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":64527354,"attachmentType":"pdf"}"><img alt="First page of “IRJET- Real Time Object Detection Using YOLOv3”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/64527354/mini_magick20200926-21016-7a3ejo.png?1601114746" /><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">IRJET- Real Time Object Detection Using YOLOv3</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="31493941" href="https://irjet.academia.edu/IRJET"><img alt="Profile image of IRJET Journal" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/31493941/9304077/11813823/s65_irjet.journal.jpg" />IRJET Journal</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2020, IRJET</p><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">5 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 = 44168535; 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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">Object detection using deep learning has achieved very good performance but there are many problems with images in real-world shooting such as noise, blurring or rotating jitter, etc. These problems have a great impact on object detection. The main objective is to detect objects using You Only Look Once (YOLO) approach. The YOLO method has several advantages as compared to other object detection algorithms. In other algorithms like Convolutional Neural Network (CNN), Fast-Convolutional Neural Network the algorithm will not look at the image completely, but in YOLO ,the algorithm looks the image completely by predicting the bounding boxes using convolutional network and finds class probabilities for these boxes and also detects the image faster as compared to other algorithms. 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