A Lightweight Traffic Sign Recognition Model Based on Improved YOLOv5

Traffic sign recognition and detection is a key technology in automatic vehicle driving and driver assistance systems. However, existing traffic sign recognition algorithms suffer from problems such as large model size, complex computation, high computational cost, which make it difficult to achieve an effective balance between detection speed and detection accuracy. This paper proposed an improved lightweight recognition algorithm, which is based on YOLOv5. This algorithm replaces the convolutional structure in the original YOLOv5 neck network with Ghost Module and C3Ghost Module, thereby reducing redundant features in the feature fusion process, lowering computational cost and the number of parameters. The structure of the PAN network was improved and the hybrid attention mechanism module CBAM was introduced to capture key information in traffic signs. Cross-layer connections were added to shorten the path of information transfer in feature pyramid network, which fused more features and improved the network feature recognition accuracy. In addition, the EIoU_Loss function was adopted as the bounding box regression loss function to improve the localization accuracy of the algorithm. The performance of the improved algorithm was also verified on the Chinese traffic sign dataset. Experimental results showed that the improved algorithm's detection accuracy was enhanced by 1.2%, while mAP@0.5 and mAP@0.5:0.95 were enhanced by 1.5% and 3.4% respectively over the existing YOLOv5 algorithm, and the overall parameter numbers and computational cost of the model were reduced by 14.5% and 16%. The proposed algorithm performs better than the current mainstream detection algorithms, has higher recognition accuracy in multiple environments, and meets the demand for real-time traffic sign recognition.