Real Time Accident Detection System using CNN

Nadiminti PulikondaUG Student, Dept. of ECE, Gates Institution of Technology, Gooty, Anantapur (Dist), Andhra Pradesh, IndiaDr. Varada RamanathAssistant Professor, Dept. of ECE, Gates Institution of Technology, Gooty, Anantapur (Dist), Andhra Pradesh, IndiaRegati RajashekarUG Student, Dept. of ECE, Gates Institution of Technology, Gooty, Anantapur (Dist), Andhra Pradesh, IndiaShaik Masood valiUG Student, Dept. of ECE, Gates Institution of Technology, Gooty, Anantapur (Dist), Andhra Pradesh, IndiaSaragaboina Naresh babuUG Student, Dept. of ECE, Gates Institution of Technology, Gooty, Anantapur (Dist), Andhra Pradesh, India

Vol 9 No 3 (2025): Volume 9, Issue 3, March 2025 | Pages: 314-319

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 19-04-2025

doi Logo doi.org/10.47001/IRJIET/2025.903045

Abstract

In India, accidents are a major cause of death. Over 80% of accident fatalities are caused by delayed assistance to victims. Accident victims can be left unattended for extended periods on lightly trafficked highways. To address this issue, we propose a system that uses deep learning to detect accidents from live CCTV video feeds. Each video frame is processed by a Convolutional Neural Network (CNN) trained to distinguish between accident and non-accident scenarios. CNNs are known for their speed, accuracy, and reduced preprocessing requirements, making them suitable for this task. With smaller datasets, CNN-based image classifiers have achieved over 95% accuracy.

Keywords

Real Time, Accident Detection System, CNN, Convolutional Neural Network


Citation of this Article

Nadiminti Pulikonda, Dr. Varada Ramanath, Regati Rajashekar, Shaik Masood vali, & Saragaboina Naresh babu. (2025). Real Time Accident Detection System using CNN. International Research Journal of Innovations in Engineering and Technology - IRJIET, 9(3), 314-319. Article DOI https://doi.org/10.47001/IRJIET/2025.903045

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