Efficiency Lightweight Model for Traffic Prohibition Sign Detection With Vehicle Camera Sensors

The detection of traffic signs through vehicle camera sensors is a crucial task for intelligent vehicles. Prohibition sign is one of the traffic signs that have a significant impact on traffic safety. However, previous research has rarely specifically focused on prohibition signs, and the accurate and rapid detection of prohibition signs remains a challenge. In this article, a dataset with the 12 most common types of Chinese prohibition signs is established, and an improved lightweight model specifically for prohibition sign detection is proposed based on YOLOv5n. In the early stage of the feature extraction network, shuffle attention (SA) is introduced to capture detailed features of the target. In the feature fusion network, a multipath weighted fusion (MWF) approach is employed to fully utilize valuable information from feature maps at different scales. In the detection output network, to prevent adverse effects on detection performance from other scales, only the $80\times 80$ detection head scale is retained. Experimental results on the established dataset show that, compared to the original model, the detection accuracy mean average precision (mAP) at 0.5 of the improved model is increased by 20.4%, with a simultaneous 31.7% reduction in the parameter count. Compared with other lightweight models such as Faster-RCNN, single shot multibox detector (SSD), and you only look once (YOLO) series, the proposed model also has higher detection accuracy and fewer model parameters. In addition, the model is deployed on Jetson TX2 and Raspberry Pi 4B with detection speeds of 21.3 and 1.8 frames per second (FPS), respectively, showcasing its potential for practical applications of vehicle camera sensors. The code of the proposed model is shared online at https://github.com/Tomming-Tong/yolov5_ms8