Sign-YOLO: A Novel Lightweight Detection Model for Chinese Traffic Sign

Abstract

Traffic sign recognition plays a crucial role in the intelligent vehicle's environment perception system. However, due to varying weather conditions, illumination, and complicated backgrounds, recognizing traffic signs becomes very challenging. A novel lightweight detection model based on YOLOv5s, namely Sign-YOLO, is proposed to overcome these challenges. Firstly, the CA (Coordinate Attention) module is incorporated into the backbone network to improve the extraction of key features. Secondly, the improved High-BiFPN is used to enhance YOLOv5s' neck structure's capability in fusing multi-scale semantic information. Finally, the improved Better-Ghost Module is employed to reduce the model's parameters and accelerate the detection speed. We used the CCTSDB2021 dataset to evaluate our model. Compared to YOLOv5s, the proposed Sign-YOLO algorithm in this paper reduces the model parameters by 0.13 M. The precision, recall, F-1 score, and mAP value have improved by 1.02%, 7.01%, 1.84%, and 4.61%, respectively. The FPS value remains around 86 fps. The results show that Sign-YOLO has achieved the optimal balance between accuracy and real-time performance. INDEX TERMSChinese traffic sign, intelligent vehicle, deep learning, lightweight model, YOLOv5s.

CTM-YOLOv8n: A Lightweight Pedestrian Traffic-Sign Detection and Recognition Model with Advanced Optimization

“…Ren et al [28] proposed a GS-FPN structure by integrating the convolutional block attention model and a new, lightweight GSConv module to replace the original feature fusion structure in YOLOv5, reducing the information loss of the feature map and ultimately achieving improved recognition performance. Song et al [29] proposed Sign-YOLO to achieve the optimal balance between accuracy and real-time performance, combining a coordinate attention module and High-BiFPN to enhance the neck structure of YOLOv5s and integrate multi-scale semantics. Meanwhile, to target TSDR in complex road conditions, YOLOv5-based improved models have been constructed by modifying the backbone, neck, head, or loss [30][31][32][33][34][35][36].…”