• Shaik Sufiyan
• Passam Srikanth
• Gedala Neeraja
• Pamu Sushma
• Kakani Srinivas

This initiative aims to address the problem of emergency services responding slowly to traffic accidents, which frequently result in fatalities. Utilizing deep learning methods, in particular Convolutional Neural Networks (CNNs), is the main goal of our study. In order to effectively detect and notify accidents. In order to extract features from photos taken by traffic surveillance cameras and enable real-time accident identification and prompt emergency services notifying, we utilize a CNN-based model. The suggested system, which makes use of the computational efficiency of GPU acceleration and enhanced algorithms, shows encouraging results in decreasing response times and raising survival rates

convolutional neural networks (CNNs), deep learning methodologies.

"Accident Detection And Alerting System Using Convolutional Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.e611-e620, June-2024, Available :http://www.jetir.org/papers/JETIR2406480.pdf

"Accident Detection And Alerting System Using Convolutional Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppe611-e620, June-2024, Available at : http://www.jetir.org/papers/JETIR2406480.pdf