Optimized YOLOv7 for traffic sign recognition

Abstract Traffic sign recognition plays a critical role in the development of autonomous driving systems and intelligent transportation networks. However, detecting small traffic signs in real-world scenarios, particularly those captured from vehicle-mounted cameras, remains a significant challenge due to their diminutive size, low resolution, and environmental noise. To address these challenges, we propose an innovative multi-strategy enhancement framework for YOLOv7, designed specifically to improve small target detection. The framework integrates several novel techniques: the SE attention mechanism is incorporated into the ELAN module of the backbone network to enhance feature discriminability; DySample replaces traditional upsampling methods in the head network to refine feature reconstruction; NWD loss is introduced as a superior alternative to CIoU loss, improving the localization accuracy of small objects; and PConv convolution is applied to reduce model parameters without sacrificing performance. Experimental results on the CCTSDB-2021 dataset demonstrate the effectiveness of these enhancements, with mAP@0.5 and mAP@0.5:0.95 improving by 6.6 and 15.2 %, respectively, compared to the original YOLOv7 model. The proposed algorithm outperforms YOLOv7 by 10.9 % in mAP@0.5 and by 11.89 % in mAP@0.5:0.95 on the TT100K dataset. Moreover, the optimized model achieves real-time inference at 83 FPS on the CCTSDB-2021 dataset, while reducing the number of parameters by 1.5 million, making it highly efficient for practical deployment in autonomous vehicles. These improvements not only enhance detection accuracy but also meet the real-time processing requirements of intelligent transportation systems.