Deep-learning-based algorithm for classifying pedestrian behavior at crosswalks

In intelligent transportation systems, pedestrian intentions at intersections must be classified to enhance pedestrian safety and optimize traffic flow. Accurately predicting pedestrian actions can significantly reduce traffic accidents and improve overall traffic management efficiency. An improved YOLOv5 model is proposed to efficiently and accurately identify pedestrian intentions. The model combines the Multi-scale Diluted Attention (MSDA) mechanism, which extends attention to different scales to capture the subtle movements of pedestrians, and the GhostNet lightweight module, which reduces the number of computational parameters and makes the model suitable for real-time applications. In addition, a new loss function, ACFloss, which combines adaptive weight-focusing loss based on contextual relationships and spatio-temporal consistency loss, is designed to understand the behavioral intent of actions better. Experimental results show that the improved YOLOv5 model achieves a classification accuracy of 93.7%, 4.5% higher than the benchmark, and the inference speed is improved by 35%. This improvement provides a reliable solution for intelligent transportation systems in complex intersection scenarios.