MSGC-YOLO: An Improved Lightweight Traffic Sign Detection Model under Snow Conditions

• Published in: Mathematics (Basel) 2024-05, Vol.12 (10), p.1539
• Main Authors: Chen, Baoxiang, Fan, Xinwei
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial intelligence; Cell division; data augmentation; Deep learning; Efficiency; Evaluation; Formability; group convolution; Influence; Mathematical research; Methods; Modules; Object recognition; Parameters; Pattern recognition; Signs; small target detection; Snow; Snow cover; Target detection; Traffic control; Traffic safety; traffic sign detection; Traffic signs; Traffic signs and signals; YOLOv8
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math12101539

Traffic sign recognition plays a crucial role in enhancing the safety and efficiency of traffic systems. However, in snowy conditions, traffic signs are often obscured by particles, leading to a severe decrease in detection accuracy. To address this challenge, we propose an improved YOLOv8-based model for traffic sign recognition. Initially, we introduce a Multi-Scale Group Convolution (MSGC) module to replace the C2f module in the YOLOv8 backbone. Data indicate that MSGC enhances detection accuracy while maintaining model lightweightness. Subsequently, to improve the recognition ability for small targets, we introduce an enhanced small target detection layer, which enhances small target detection accuracy while reducing parameters. In addition, we replaced the original BCE loss with the improved EfficientSlide loss to improve the sample imbalance problem. Finally, we integrate Deformable Attention into the model to improve the detection efficiency and performance of complex targets. The resulting fused model, named MSGC-YOLOv8, is evaluated on an enhanced dataset of snow-covered traffic signs. Experimental results show that the MSGC-YOLOv8 model is used for snow road traffic sign recognition. Compared with the YOLOv8n model mAP@0.5:0.95, mAP@0.5:0.95 is increased by 17.7% and 18.1%, respectively, greatly improving the detection accuracy. Compared with the YOLOv8s model, while the parameters are reduced by 59.6%, mAP@0.5 only loses 1.5%. Considering all aspects of the data, our proposed model shows high detection efficiency and accuracy under snowy conditions.