Traffic Sign Recognition Using Improved YOLOv7 Model

Traffic sign recognition is a important component of environmental perception technology, with significant potential in the field of autonomous vehicles. Traditional recognition methods are susceptible to factors such as lighting conditions, extreme weather, and distance, leading to mismatches. To tackle this problem, this study proposed an improved YOLOv7-based algorithm for traffic sign recognition, aimed at enhancing the accuracy of small traffic sign recognition. Firstly, an ACmix attention mechanism module is inserted after the SPPCSPC layer in the backbone of the YOLOv7 network to enhance feature extraction capability and adaptability to complex scenarios. Secondly, the SIoU loss function is employed to replace the original YOLOv7 bounding box loss function, thus improving the precision of bounding box regression and localization. Extensive experiments are conducted to validate the effectiveness of the proposed algorithm. The improved algorithm is tested and evaluated using the TT100K dataset, achieving a recognition accuracy of 94.23%, a recall rate of 78.1%, mAP@0.5 of 86.5%, and mAP@0.5:0.95 of 65.6%, surpassing the original YOLOv7 network. Moreover, the frame rate remains stable at 82 frames per second. The evaluations on traffic sign datasets demonstrate the accuracy and effectiveness of the proposed method.