• Dr. G. Arun Sampaul Thomas
• P.Rishi preetham
• S.Vaishnavi
• M.Harika
• P.Vigneshwar reddy

The Deep Learning Based Road Accident Detection and Alert System is designed to improve the response time to traffic accidents by leveraging deep learning algorithms for real-time detection and alerting. Utilizing advanced neural networks like VGG16(Visual Geometry Group), the system processes CCTV footage to identify accidents with high accuracy. Immediate alerts ensure prompt assistance, enhancing road safety and saving lives.Road accidents are a significant global concern, contributing to numerous fatalities and injuries annually. Timely detection and reporting of road accidents are critical for minimizing response times and improving survival rates. This study proposes a deep learning-based system for automatic road accident detection and alert generation. The system utilizes advanced neural network architectures to analyze real-time data from surveillance cameras, dash cams, or other sensors. It identifies accident scenarios based on visual and contextual cues, leveraging techniques such as convolutional neural networks (CNNs) for image processing and temporal data analysis. Once an accident is detected, the system sends instant alerts to emergency services and nearby users, ensuring prompt intervention. Experimental results demonstrate high accuracy and reliability, validating the system’s effectiveness in diverse real-world scenarios. This solution represents a significant step towards enhancing road safety and reducing emergency response times through the integration of artificial intelligence.

DEEP LEARNING BASED ROAD ACCIDENT DETECTION AND ALERTING SYSTEM

"DEEP LEARNING BASED ROAD ACCIDENT DETECTION AND ALERTING SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.e515-e522, March-2025, Available :http://www.jetir.org/papers/JETIR2503472.pdf

"DEEP LEARNING BASED ROAD ACCIDENT DETECTION AND ALERTING SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppe515-e522, March-2025, Available at : http://www.jetir.org/papers/JETIR2503472.pdf