As autonomous and assisted driving technologies progress rapidly, the significance of traffic sign recognition intensifies. Currently, the detection accuracy of algorithms for traffic sign recognition remains suboptimal, particularly when identifying small traffic signs amid complex backgrounds and under inadequate lighting, leading frequently to errors in detection. This paper introduces an enhanced method for small traffic sign recognition, underpinned by an improved version of YOLOv7. Initially, The Spatial Pyramid Pooling Fast and Cross-Stage Partial Connection (SPPFCSPC) strategy was used to improve the feature extraction of small targets. Subsequently, a Shuffle Attention-CARAFE (S-CARAFE) up-sampling operator is crafted. S-CARAFE refocuses on key features within the input data, boosting the information detail and improving feature recombination. Finally, the introduction of a new Normalized Wasserstein Distance (NWD) method resolves the traditional IoU measurement's sensitivity to small-target traffic signs. Experimental results show that the mAP@0.5 and mAP@0.5:0.9 values of the model trained on the TT100K dataset are increased by 3.48% and 2.29%, respectively. In the comparison with algorithms of a similar type, the proposed method achieved improvements of 2.61% and 2.12% in mAP@50 and mAP@50:95, respectively. Additionally, the algorithm's improvements are validated on the small-target characteristics of the CCTSDB dataset and the sorted foreign traffic sign dataset, effectively elevating the recognition of small traffic signs across varying environments, consequently advancing the traffic sign recognition capacity of autonomous driving systems.