LAUSR

Automatic traffic sign detection has great potential for intelligent vehicles. In recent years, traffic sign detection has made significant progress with the rise of deep learning. Detecting small traffic signs in real-world scenarios is still a challenging problem due to the complex and variable traffic environment. In this paper, a model with a small number of parameters is proposed to improve the accuracy of small traffic sign detection. Firstly, the cross-stage attention network module is proposed to enhance the feature extraction capability of the network. Secondly, a dense neck structure is proposed to make the detail information and semantic information fully fused. Finally, for the model’s loss function, SIOU with direction information is introduced to optimize the model’s training process. Tests on the challenging public datasets TT100K, CCTSDB2021, and VOC show that our approach achieves significant performance improvement with the minimum number of parameters compared to existing algorithms.