YOLO-FLC: Lightweight Traffic Sign Detection Algorithm

High real-time requirements, complex backgrounds, and small targets are major challenges in traffic sign recognition. Mainstream algorithms still suffer from issues such as complex model structures, redundant parameters, and high computational demands. Therefore, striking a balance between model lightweighting and accuracy has become a new challenge. This paper is based on the single-stage YOLOv5 algorithm framework and presents an optimized design of YOLO-F detection model with fewer parameters, high detection accuracy, the ability to effectively identify small targets, and easy deployment. The detection algorithm achieves an accuracy of 87.5%, a recall rate of 81.7%, and a detection speed of 89FPS. Furthermore, the detection model undergoes LAMP channel pruning to significantly reduce parameters and computational load while maintaining accuracy. Additionally, the pruned model undergoes distillation to enhance accuracy. Experimental results demonstrate that the improved YOLO-FLC model, compared to YOLOv5s, achieves reductions of 80.49%, 39.38%, and 74.22% in model parameters, computational load, and weight, respectively, while increasing mAP to 0.881, up by 2.6%. The FPS reaches 99, effectively balancing real-time performance and accuracy.