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false; window.loswp.work = {"work":{"id":87628836,"created_at":"2022-09-30T08:52:45.116-07:00","from_world_paper_id":216323316,"updated_at":"2025-02-01T13:19:45.754-08:00","_data":{"publisher":"Egypts Presidential Specialized Council for Education and Scientific Research","ai_title_tag":"YOLOv3 Performance Evaluation in Object Detection","grobid_abstract":"You Only Look Once version 3 (YOLOv3) is a deep learning model for object detection and classification. It is a single neural network architecture model that uses features from the feeding images and predicts bounding box for all classes of image simultaneously. This paper descript an experimental work for train the deep learning model based on YOLOv3 architecture implemented using Tensor Flow as a deep learning framework. The training process had been done using the data-set PASCAL VOC 2007 and data-set PASCAL VOC 2012 and using The Adaptive Moment Estimation Optimizer (ADM optimizer). The trained model is then tested by using the VOC 2007 test data-set. The final results evaluate the YOLOv3 deep learning model performance for object detection and classification.","publication_name":"Menoufia Journal of Electronic Engineering Research","grobid_abstract_attachment_id":"91786325"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Evaluation of Deep Learning YOLOv3 Algorithm for Object Detection and Classification","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [45815667]; 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"><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":91786325,"attachmentType":"pdf"}"><img alt="First page of “Evaluation of Deep Learning YOLOv3 Algorithm for Object Detection and Classification”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/91786325/mini_magick20220930-1-qs1dun.png?1664554782" /><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">Evaluation of Deep Learning YOLOv3 Algorithm for Object Detection and Classification</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="45815667" href="https://independent.academia.edu/NabilIsmail3"><img alt="Profile image of Nabil Ismail" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/45815667/17425592/17512742/s65_nabil.ismail.jpg" />Nabil Ismail</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">Menoufia Journal of Electronic Engineering Research</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">6 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 = 87628836; 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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">You Only Look Once version 3 (YOLOv3) is a deep learning model for object detection and classification. It is a single neural network architecture model that uses features from the feeding images and predicts bounding box for all classes of image simultaneously. This paper descript an experimental work for train the deep learning model based on YOLOv3 architecture implemented using Tensor Flow as a deep learning framework. The training process had been done using the data-set PASCAL VOC 2007 and data-set PASCAL VOC 2012 and using The Adaptive Moment Estimation Optimizer (ADM optimizer). The trained model is then tested by using the VOC 2007 test data-set. The final results evaluate the YOLOv3 deep learning model performance for object detection and classification.</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":91786325,"attachmentType":"pdf","workUrl":"https://www.academia.edu/87628836/Evaluation_of_Deep_Learning_YOLOv3_Algorithm_for_Object_Detection_and_Classification"}">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":91786325,"attachmentType":"pdf","workUrl":"https://www.academia.edu/87628836/Evaluation_of_Deep_Learning_YOLOv3_Algorithm_for_Object_Detection_and_Classification"}"><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-premium-marketing"></div></div><div class="ds-signup-banner ds-signup-banner-premium-marketing"><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="premium-banner-content"><div class="left"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><span>Get access to the world's latest research</span></div><div class="right"><div class="card free"><div class="header">Free</div><div class="feature-list"><div class="feature"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">check</span><span>Download one paper at a time</span></div><div class="feature"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">check</span><span>Save papers to bookmarks</span></div><div class="feature"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">check</span><span>Basic search</span></div></div><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--small ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"premium-banner-desktop-free"}">Sign up for free</button></div><div class="card premium"><div class="pill">Recommended</div><div class="header premium">Premium</div><div class="feature-list"><div class="feature"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">check</span><span>Get highly curated PDF packages</span></div><div class="feature"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">check</span><span>Track your impact with Mentions</span></div><div class="feature"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">check</span><span>Access advanced search filters</span></div><div class="feature"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">check</span><span>Support Academia’s mission</span></div><div class="feature"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">check</span><span>Create your personal website</span></div></div><button class="ds2-5-button ds2-5-button--small ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"premium-banner-desktop-upgrade","submitText":"Try Premium for $1"}">Try Premium for $1</button></div></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. 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Object detection is a growing field of research in the field of computer vision. The ability to identify and classify objects, either in a single scene or in more than one frame, has gained huge importance in a variety of ways, as while operating a vehicle, the operator could even lack attention that could lead to disastrous collisions. In attempt to improve these perceivable problems, the Autonomous Vehicles and ADAS (Advanced Driver Assistance System) have considered to handle the task of identifying and classifying objects, which in turn use deep learning techniques such as the Faster Regional Convoluted Neural Network (F-RCNN), the You Only Look Once Model (YOLO), the Single Shot Detector (SSD) etc. to improve the precision of object detection. YOLO is a powerful technique as it achieves high precision whilst being able to manage in real time. This paper explains the architecture and working of YOLO algorithm for the purpose of detecting and classifying objects, trained on the classes from COCO dataset.</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":"IJERT-Object Detection and Classification using YOLOv3","attachmentId":66162765,"attachmentType":"pdf","work_url":"https://www.academia.edu/45661590/IJERT_Object_Detection_and_Classification_using_YOLOv3","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/45661590/IJERT_Object_Detection_and_Classification_using_YOLOv3"><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="74581184" 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/74581184/Real_Time_Object_Detection_Using_YOLOv3">Real Time Object Detection Using YOLOv3</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="219977968" href="https://independent.academia.edu/shubhampatil629">shubham patil</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="132373791" href="https://independent.academia.edu/omasurekar">omkar masurekar</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2020</p><p class="ds-related-work--abstract ds2-5-body-sm">1,2,3,4 Student, Department of Computer Engineering, TEC, University of Mumbai, Mumbai, India ---------------------------------------------------------------------***--------------------------------------------------------------------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 ...</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 YOLOv3","attachmentId":82683426,"attachmentType":"pdf","work_url":"https://www.academia.edu/74581184/Real_Time_Object_Detection_Using_YOLOv3","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/74581184/Real_Time_Object_Detection_Using_YOLOv3"><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="50954646" 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/50954646/IRJET_OBJECT_DETECTION_AND_CLASSIFICATION_USING_YOLOV3">IRJET- OBJECT DETECTION AND CLASSIFICATION USING YOLOV3</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--metadata ds2-5-body-xs">IRJET, 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">Object detection has several advantages in computer vision technologies. It is used in image retrieval, security, observations, etc. The goal of object detection system is object localization and identifying the category to which the object belongs. In this paper, a deep learning algorithm YOLO (You Only Look Once) is used for object detection and classification. This proposed method yields mean average precision (mAP) of 95% for traffic scenario images in identifying traffic lights, car, bus, person and motorcycle.</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":"IRJET- OBJECT DETECTION AND CLASSIFICATION USING YOLOV3","attachmentId":68832119,"attachmentType":"pdf","work_url":"https://www.academia.edu/50954646/IRJET_OBJECT_DETECTION_AND_CLASSIFICATION_USING_YOLOV3","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/50954646/IRJET_OBJECT_DETECTION_AND_CLASSIFICATION_USING_YOLOV3"><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="72774189" 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/72774189/Object_Detection_through_Modified_YOLO_Neural_Network">Object Detection through Modified YOLO Neural Network</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="56170611" href="https://hust-cn.academia.edu/BelalAhmad">Belal Ahmad</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Scientific Programming</p><p class="ds-related-work--abstract ds2-5-body-sm">In the field of object detection, recently, tremendous success is achieved, but still it is a very challenging task to detect and identify objects accurately with fast speed. Human beings can detect and recognize multiple objects in images or videos with ease regardless of the object’s appearance, but for computers it is challenging to identify and distinguish between things. In this paper, a modified YOLOv1 based neural network is proposed for object detection. The new neural network model has been improved in the following ways. Firstly, modification is made to the loss function of the YOLOv1 network. The improved model replaces the margin style with proportion style. Compared to the old loss function, the new is more flexible and more reasonable in optimizing the network error. Secondly, a spatial pyramid pooling layer is added; thirdly, an inception model with a convolution kernel of 1 ∗ 1 is added, which reduced the number of weight parameters of the layers. Extensive experimen...</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 through Modified YOLO Neural Network","attachmentId":81567901,"attachmentType":"pdf","work_url":"https://www.academia.edu/72774189/Object_Detection_through_Modified_YOLO_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/72774189/Object_Detection_through_Modified_YOLO_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="4" data-entity-id="99058470" 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/99058470/A_Literature_Review_of_Object_Detection_using_YOLOv4_Detector">A Literature Review of Object Detection using YOLOv4 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="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), 2023</p><p class="ds-related-work--abstract ds2-5-body-sm">Object detection is an advanced form of image classification where a neural network predicts objects in an image and points them out in the form of bounding boxes. Compared to the approach taken by object detection algorithms before YOLO, which repurpose classifiers to perform detection, YOLO proposes the use of an end-to-end neural network that makes predictions of bounding boxes and class probabilities all at once. Object detection not solely includes classifying and recognizing objects in an image but also includes localizing those objects and attracts bounding boxes around them. Its application includes field like face detection, detecting vehicles, autonomous vehicles and pedestrians on streets.</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 Literature Review of Object Detection using YOLOv4 Detector","attachmentId":100245608,"attachmentType":"pdf","work_url":"https://www.academia.edu/99058470/A_Literature_Review_of_Object_Detection_using_YOLOv4_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/99058470/A_Literature_Review_of_Object_Detection_using_YOLOv4_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="5" data-entity-id="67736659" 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/67736659/Real_Time_Object_Detection_Using_Yolo">Real Time Object Detection Using Yolo</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">IJRASET, 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">Object detection is related to computer vision and involves identifying the kinds of objects that have been detected. It is challenging to detect and classify objects. Recent advances in deep learning have allowed it to detect objects more accurately. In the past, there were several methods or tools used: R-CNN, Fast-RCNN, Faster-RCNN, YOLO, SSD, etc. This research focuses on "You Only Look Once" (YOLO) as a type of Convolutional Neural Network. Results will be accurate and timely when tested. So, we analysed YOLOv3's work by using Yolo3-tiny to detect both image and video objects.</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 Yolo","attachmentId":78453430,"attachmentType":"pdf","work_url":"https://www.academia.edu/67736659/Real_Time_Object_Detection_Using_Yolo","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/67736659/Real_Time_Object_Detection_Using_Yolo"><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="122355932" 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/122355932/Enhancing_Object_Detection_Accuracy_Through_Custom_Dataset_Using_Yolo">Enhancing Object Detection Accuracy Through Custom Dataset Using Yolo</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), 2024</p><p class="ds-related-work--abstract ds2-5-body-sm">Potholes pose significant risks to road safety and vehicle maintenance, leading to accidents and costly repairs. Traditional methods of pothole detection are often labour-intensive and time-consuming. In this study, we propose an innovative approach to pothole detection using YOLOv8, a state-of-the-art object detection algorithm. By harnessing the power of deep learning, our system can accurately identify and locate potholes in real-time video streams from traffic cameras and vehicles. We employ YOLOv8, an advanced variant of the You Only Look Once (YOLO) algorithm, known for its speed and accuracy in real-time object detection tasks. Leveraging a large annotated dataset of road images, we fine-tune the YOLOv8 model to specifically detect potholes. Our trained model is capable of identifying various pothole sizes and shapes, even in challenging lighting and weather conditions. The goal of this study is to apply different YOLO models for pothole detection. Three state-of-the-art object detection frameworks (i.e., YOLOv4,YOLOv4-tiny, and YOLOv5s) are experimented to measure their performance involved in real-time responsiveness and detection accuracy using the image set. The image set is identified by running the deep convolutional neural network (CNN) on several deep learning pothole detectors. After collecting a set of 600 images in 720×720 pixels resolution that captures various types of potholes on different road surface conditions, the set is divided into training, testing, and validation subset [1]. I. PROBLEM STATEMENT To implement real-time object detection and recognition in an images captured by webcam and videos in dynamic environment using deep learning model and YOLO ." The primary goal is to detect and recognition Objects in Real-time. We require rich data, all things considered. We need to observe the different type of objects which are moving in respect to the camera. It will help us with perceiving and in recognizing different objects collaboration and interaction. II.</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":"Enhancing Object Detection Accuracy Through Custom Dataset Using Yolo","attachmentId":117037844,"attachmentType":"pdf","work_url":"https://www.academia.edu/122355932/Enhancing_Object_Detection_Accuracy_Through_Custom_Dataset_Using_Yolo","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/122355932/Enhancing_Object_Detection_Accuracy_Through_Custom_Dataset_Using_Yolo"><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="115187077" 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/115187077/Comparative_Study_of_Some_Deep_Learning_Object_Detection_Algorithms_R_CNN_FAST_R_CNN_FASTER_R_CNN_SSD_and_YOLO">Comparative Study of Some Deep Learning Object Detection Algorithms: R-CNN, FAST R-CNN, FASTER R-CNN, SSD, and YOLO</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="182343151" href="https://afit.academia.edu/OluwaseyiOlorunshola">Dr. Oluwaseyi Olorunshola</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Due to its numerous applications and new technological advancements, object detection has gained more attention in the last few years. This study examined various uses of some deep learning object detection algorithms. These algorithms are divided into two-stage detectors like Region Based Convolutional Neural Network (R-CNN), Fast Region Based Convolutional Neural Network (Faster R-CNN), and Faster Region Based Convolutional Neural Network (Faster R-CNN), and one-stage detectors like Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) algorithms that are used in text and face detection, image retrieval, security, surveillance, traffic control, traffic sign/light detection, pedestrian detection and in medical areas among others. This research primarily focuses on three applications: drone surveillance, applications relating to traffic, and medical fields. Findings from the performed analysis indicate that YOLO stands out as the predominant algorithm for drone surveillance among different deep learning models used in various application fields and being a one-stage detector. In terms of usage in traffic-related applications, SSD proved to be a prominent one-stage detector alongside Faster R-CNN which gained popularity as a two-stage detector preferred for applications in the medical field.</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":"Comparative Study of Some Deep Learning Object Detection Algorithms: R-CNN, FAST R-CNN, FASTER R-CNN, SSD, and YOLO","attachmentId":111668181,"attachmentType":"pdf","work_url":"https://www.academia.edu/115187077/Comparative_Study_of_Some_Deep_Learning_Object_Detection_Algorithms_R_CNN_FAST_R_CNN_FASTER_R_CNN_SSD_and_YOLO","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/115187077/Comparative_Study_of_Some_Deep_Learning_Object_Detection_Algorithms_R_CNN_FAST_R_CNN_FASTER_R_CNN_SSD_and_YOLO"><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><p class="ds-related-work--abstract ds2-5-body-sm">Object detection is a critical and complex problem in computer vision, and deep neural networks have significantly enhanced their performance in the last decade. There are two primary types of object detectors: two stage and one stage. Two-stage detectors use a complex architecture to select regions for detection, while one-stage detectors can detect all potential regions in a single shot. When evaluating the effectiveness of an object detector, both detection accuracy and inference speed are essential considerations. Two-stage detectors usually outperform one-stage detectors in terms of detection accuracy. However, YOLO and its predecessor architectures have substantially improved detection accuracy. In some scenarios, the speed at which YOLO detectors produce inferences is more critical than detection accuracy. This study explores the performance metrics, regression formulations, and single-stage object detectors for YOLO detectors. Additionally, it briefly discusses various YOLO variations, including their design, performance, and use cases.</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="63355611" 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/63355611/Literature_Survey_on_Object_Detection_using_YOLO">Literature Survey on Object Detection using YOLO</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="161978239" href="https://vtua.academia.edu/ATHIYAMARIUM">ATHIYA MARIUM</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2020</p><p class="ds-related-work--abstract ds2-5-body-sm">1,3Professor, Dept. of Information Science and Engineering, R V College, Karnataka, INDIA 2,4Dept. of Information Science and Engineering, R V College, Karnataka, INDIA ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Object detection is important for computer vision. The problems such as noise, blurring and rotating jitter, etc. with images in real-world have an important impact on object detection. The objects can be detected in real time using YOLO (You only look once), an algorithm based on convolutional neural networks. This paper addresses the various modifications done to YOLO network which improves the efficiency of object detection.</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":"Literature Survey on Object Detection using YOLO","attachmentId":75812165,"attachmentType":"pdf","work_url":"https://www.academia.edu/63355611/Literature_Survey_on_Object_Detection_using_YOLO","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/63355611/Literature_Survey_on_Object_Detection_using_YOLO"><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":91786325,"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":91786325,"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_91786325" 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="48839724" 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/48839724/Real_Time_Object_Detection_using_YOLO_A_review">Real-Time Object Detection using YOLO: A review</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="193375969" href="https://independent.academia.edu/LakshiniKuganandamurthy">Lakshini Kuganandamurthy</a><span>, </span><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="147848209" href="https://iuni-saarland.academia.edu/UpulieHandalage">Upulie Handalage</a></div><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 YOLO: A review","attachmentId":67257544,"attachmentType":"pdf","work_url":"https://www.academia.edu/48839724/Real_Time_Object_Detection_using_YOLO_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-related-work-grid-card-view-pdf" href="https://www.academia.edu/48839724/Real_Time_Object_Detection_using_YOLO_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-related-work-sidebar-card" data-collection-position="1" data-entity-id="104852572" 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/104852572/A_Comparative_Study_of_YOLOv5_and_YOLOv7_Object_Detection_Algorithms">A Comparative Study of YOLOv5 and YOLOv7 Object Detection Algorithms</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="182343151" href="https://afit.academia.edu/OluwaseyiOlorunshola">Dr. Oluwaseyi Olorunshola</a><span>, </span><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="3268524" href="https://pmb2109.academia.edu/MartinsIrhebhude">Martins E . Irhebhude PhD</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Computing and Social Informatics, 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":"A Comparative Study of YOLOv5 and YOLOv7 Object Detection Algorithms","attachmentId":104469986,"attachmentType":"pdf","work_url":"https://www.academia.edu/104852572/A_Comparative_Study_of_YOLOv5_and_YOLOv7_Object_Detection_Algorithms","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-related-work-grid-card-view-pdf" href="https://www.academia.edu/104852572/A_Comparative_Study_of_YOLOv5_and_YOLOv7_Object_Detection_Algorithms"><span class="ds2-5-text-link__content">View 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Detection Using YOLOv3","attachmentId":64527354,"attachmentType":"pdf","work_url":"https://www.academia.edu/44168535/IRJET_Real_Time_Object_Detection_Using_YOLOv3","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-related-work-grid-card-view-pdf" href="https://www.academia.edu/44168535/IRJET_Real_Time_Object_Detection_Using_YOLOv3"><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-related-work-sidebar-card" data-collection-position="3" data-entity-id="110953191" 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/110953191/Yolo_ros_Convolutional_Neural_Networks_object_detection">Yolo/ros/Convolutional Neural Networks/object detection</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="206996810" href="https://independent.academia.edu/AyaMahmood3">Aya Mahmood</a></div><p class="ds-related-work--metadata ds2-5-body-xs">yolo, 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":"Yolo/ros/Convolutional Neural Networks/object detection","attachmentId":108610215,"attachmentType":"pdf","work_url":"https://www.academia.edu/110953191/Yolo_ros_Convolutional_Neural_Networks_object_detection","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span 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