VEHICLE ANOMALY DETECTION USING DEEP LEARNING AND AI

Abstract

Road safety and traffic management have become major concerns due to the rapid increase in vehicles. This project presents an intelligent Vehicle Anomaly Detection System using Deep Learning and Artificial Intelligence to monitor traffic behavior in real time. The system processes live video streams and applies a YOLO-based deep learning model to detect and classify vehicles accurately. By tracking vehicle movement and lane usage, the system identifies abnormal driving behaviors such as lane violations, wrong-direction movement, sudden stops, and overspeeding. When an anomaly is detected, the system immediately generates alerts for quick response. This automated approach reduces dependency on manual monitoring and minimizes human error. The proposed system improves road safety, enhances traffic efficiency, and supports smart transportation infrastructure. The solution is scalable, reliable, and suitable for real-world intelligent transportation systems.