Deep Learning Approaches for Traffic Accident Detection

Accident detection is a crucial component of intelligent transportation systems as it helps minimize emergency response delays and increases the likelihood of saving lives. This work introduces a deep learning framework based on Convolutional Neural Networks (CNNs) to identify road accidents from traffic surveillance footage. The model utilizes both RGB image frames and optical flow features to enhance detection accuracy, achieving a performance of nearly 87%. Once an accident is recognized, the system captures the incident frame, determines the location using Google Maps, and sends immediate alerts to nearby hospitals and police departments through cloud services. This real-time detection mechanism enables quicker emergency assistance, strengthens road safety, and supports the development of safer smart city infrastructures.