Enhancing Road Traffic Safety with YOLO V7 Object Detection

Enhancing Road Traffic Safety with YOLO V7 Object Detection

Registration ID: IJNRD_217110

Published ID: IJNRD2404056

DOI: Click Here to Get

Shruti Pramod Akkewar , Sonal Narendra Naitam , Snehal Satish Barmase , Purva Naresh Telmasre , Sakshi Sunil Barai

Keywords— Real-time object detection, road traffic safety, Bounding boxes,Intersection over Union (IOU), Anchor box, NonMax Suppression.

Abstract— Road traffic safety is a crucial concern around the world, as the number of vehicles on the road increases, so does the risk of accidents and collisions. Object detection technology has developed as an effective technique for improving road traffic safety. This study presents a thorough examination of the most recent advances in object identification systems and their applications in road traffic safety. The paper opens by providing an overview of the issues and risks involved with road traffic, highlighting the importance of enhanced safety measures. It then digs into a full review of object identification strategies, ranging from traditional methods to cutting-edge deep learning models, demonstrating their capacities to identify vehicles, pedestrians, cyclists, and other road items.It investigates how these technologies improve real-time monitoring, collision avoidance, and traffic management. Furthermore, the article looks into object detection for traffic law enforcement and monitoring, emphasizing its significance in improving security and lowering accidents. It outlines prospective future research directions, such as the development of powerful, real-time object detection systems and their application to smart city initiatives. Keywords— Real-time object detection, road traffic safety, Bounding boxes,Intersection over Union (IOU), Anchor box, NonMax Suppression.Abstract— Road traffic safety is a crucial concern around the world, as the number of vehicles on the road increases, so does the risk of accidents and collisions. Object detection technology has developed as an effective technique for improving road traffic safety. This study presents a thorough examination of the most recent advances in object identification systems and their applications in road traffic safety. The paper opens by providing an overview of the issues and risks involved with road traffic, highlighting the importance of enhanced safety measures. It then digs into a full review of object identification strategies, ranging from traditional methods to cutting-edge deep learning models, demonstrating their capacities to identify vehicles, pedestrians, cyclists, and other road items.It investigates how these technologies improve real-time monitoring, collision avoidance, and traffic management. Furthermore, the article looks into object detection for traffic law enforcement and monitoring, emphasizing its significance in improving security and lowering accidents. It outlines prospective future research directions, such as the development of powerful, real-time object detection systems and their application to smart city initiatives.

Published Paper   E-Certificate

"Enhancing Road Traffic Safety with YOLO V7 Object Detection", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 4, page no.a434-a438, April-2024, Available :https://ijnrd.org/papers/IJNRD2404056.pdf

Volume 9 Issue 4, April-2024

Pages : a434-a438

Paper Reg. ID: IJNRD_217110

Published Paper Id: IJNRD2404056

Downloads: 000121189

Research Area: Computer Engineering 

Country: Nagpur, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404056.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404056

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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