Research on Traffic Sign Recognition Algorithm Based on YOLOv5
DOI:
https://doi.org/10.62051/ijcsit.v2n2.05Keywords:
YOLOv5; CBAM; Convolutional neural network; Traffic sign recognitionAbstract
With the continuous development of driving assistance system and automatic driving technology in recent years, the requirements for traffic sign recognition technology are becoming higher and higher. Although the current mainstream target detection technology has been guaranteed in real-time and accuracy, due to the complexity of traffic signs and the fact that most traffic signs in the actual scene are small and dense, the identification accuracy of small target traffic signs is low. Based on the above situation, an improved YOLOv5 traffic sign recognition algorithm is proposed. We add CBAM attention mechanism to the model to improve the feature extraction ability of the target object. The structure based on the feature pyramid is improved to strengthen the features of small targets. The upsampling algorithm is replaced to save computing power. The overall detection and recognition ability of the model is improved. The loss function is improved to DIoU loss function, which enhances the robustness of the model.
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Copyright (c) 2024 Zhichen Li, Hua Huo
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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