An AI Hybrid Model for Road Accident Detection based on YOLO and CNN Algorithm

Accurate and up-to-date accident detection is crucial to improve road safety and reduce emergency response lag. Conventional methods for detecting accidents are usually based on manual observation, sparse data, or imprecise sensors, which do not support scalability and real-world accuracy. The research work suggests a hybrid AI system combining You Only Look Once (YOLO) for best object detection and Convolutional Neural Networks (CNNs) for best severity classification of accidents. The system accurately detects accidents from live surveillance video feeds and classifies them by severity into light, moderate, and severe classes, thus immediately alerting emergency services with minimal latency. Experimental findings prove that our method outcompetes standard procedures, and over $94 \%$ accuracy and precision are maintained. Moreover, the model is aided by deep learning methods in powerful feature extraction to make the model resistant to differences in weather and lighting conditions. This work brings forth an intelligent transportation system (ITS) by presenting an effective and scalable means of automated accident detection in the computer domain and proves that the AI technology holds the ability to prevent traffic accidents and save lives.