Real Time Vehicle Collision Detection Using Deep Learning

Geetansh Sharma , Pranav Maheshwari , Shashank Singh , Aman Agarwal , Neha Ahlawat

Accident detection, Intelligent transportation systems, Deep learning, Object detection, YOLOv3, Real-time performance..

Accident detection is an essential application in intelligent transportation systems for the safety of drivers and passengers. Deep learning-based object identification algorithms have significantly improved in recent years in spotting objects in real time. YOLO (You Only Look Once) is one such model that has gained popularity due to its real-time performance and high accuracy. We propose an accident detection system in this paper using YOLOv3, the most recent version of YOLO. The proposed system is designed to detect three types of accidents, namely vehicle rollover, rear-end collision, and head-on collision. The system uses a pre-trained YOLOv3 model trained on the COCO dataset, which is fine-tuned on a custom dataset of accident images. The proposed system achieves an average precision of 0.94 for vehicle rollover detection, 0.93 for rear-end collision detection, and 0.92 for head-on collision detection. The system also shows promising results in terms of real-time performance, with an average processing time of 0.03 seconds per frame on an NVIDIA GeForce GTX 1080 Ti GPU. The proposed system can be integrated into intelligent transportation systems to provide real-time accident detection and alerting, improving the safety of drivers and passengers on the road.

Published Paper   E-Certificate

"Real Time Vehicle Collision Detection Using Deep Learning", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 7, page no.323 - 329, July-2023, Available :https://ijsdr.org/papers/IJSDR2307044.pdf

Volume 8 Issue 7, July-2023

Pages : 323 - 329

Paper Reg. ID: IJSDR_206377

Published Paper Id: IJSDR2307044

Downloads: 000347255

Research Area: Computer Science & Technology 

Country: Delhi, Delhi, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2307044

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2307044

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

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