Attention-Guided Convolutional Neural Networks for Traffic Anomaly Detection

Abstract

This study examines Attention Enhanced Convolutional Neural Networks (AE-CNNs) as a potential approach to identifying traffic issues on intricate, constantly evolving highways. By integrating attention processes into conventional convolutional neural network topologies, the proposed approach prioritizes critical spatial and temporal input. This facilitates the identification of traffic congestion, vehicle anomalies, and collisions. Using channel and spatial attention modules, this approach enhances feature representation and eliminates extraneous data. It surpasses conventional methodologies due to its utilization of CNN. The system is more accurate, robust, and generates fewer false alarms, as evidenced by experimental results with a variety of traffic datasets. The results indicate that AE-CNNs enhance the efficiency and safety of urban transportation by enhancing smart transportation and real-time traffic monitoring.