Convolutional Neural Networks for Enhanced Real-Time Traffic Incident Detection and Image Classification

Traffic incident detection and classification are essential aspects of enhancing road safety and efficiency. A new approach to enhancing real-time traffic incident detection and image classification with the help of Convolutional Neural Networks (CNNs), viz., Alex Net, is presented in this paper. Alex Net, a deep CNN architecture that is widely used for image recognition, is employed to classify traffic images and videos for incident identification. The system makes use of the ability of the model to learn robust features to identify images as different events, such as accidents, roadblocks, and vehicle breakdowns. Low latency and high accuracy incident identification is done by the system utilizing real-time traffic camera data and Alex Net. Differentiation between different types of events. This is for the sake of enhancing reaction time, enabling effective traffic management, and differentiating between events. AlexNet enables 92% accuracy in real-time traffic detection and image classification, enhancing the efficiency of intelligent transportation systems. The results demonstrate how deep learning (DL) methods can revolutionise traffic monitoring systems and improve safety protocols for drivers.