Abstract
To solve the issue of standard traffic sign identification algorithms' poor detection accuracy, an improved YOLOv5 algorithm for traffic sign recognition is proposed. To begin, the YOLOv5 algorithm's backbone network is enhanced, and the original YOLOv5 network's backbone feature extraction network is replaced with the ultra-lightweight convolutional neural network MobileOne. To increase the model's focus on additional locations, the Coordinate Attention module's introduction, it incorporates location information into channel attention and performs multi-scale processing and feature fusion. Experiments demonstrate that the new lightweight network model is only 76% the size of the original YOLOv5 model, and the mAP on the dataset reaches 96.2%. This strategy significantly decreases the amount of model parameters and procedures required to ensure detection accuracy, while also improving detection speed and accuracy.
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