A Lightweight Traffic Signal Video Stream Detection Model Based on Depth-Wise Separable Convolution

With the rapid development of Intelligent Traffic Systems (ITS), traffic signal detection has become a hot research issue in various countries. In order to address the challenges of high parameter count, high power consumption, and deployment difficulties on edge devices for the MCA (Multidimensional Collaborative Attention)-YOLOv5-ACON (Activate or Not) model, a lightweight traffic signal video stream detection model based on depth-wise separable convolutions (DSC) was proposed. First, an enhanced MCA-YOLOv5-ACON was elucidated. Then, the backbone network in this model was substituted with MobileNetv3, and ordinary convolutions were replaced with DSC in the PANet section, with a view to achieving further compression. Finally, a comprehensive signal fault determination logic was devised with the objective of identifying common fault types. Results showed that the optimized MobileNetv3-MCA-YOLOv5 model occupied only 19.37% of the original memory usage, with an mAP of 93.57%. While the mAP decreased slightly, Precision increased from 98.15% to 98.53%, and the video streaming detection speed improved from 25.20 fps to 33.34 fps. The improved lightweight model balances high precision and real-time performance, making it more suitable for deployment on edge devices.