SOD-YOLO: A New Small Object Traffic Sign Recognition Network
Xunjia Yin, Xiao-Ming Wu, Xiangzhi Liu · 2024
Traffic sign recognition is a challenging task for unmanned systems, especially the problem of detecting small targets. During traffic sign recognition (TSR), traffic sign targets are small, represented by a small number of pixels in the image, and lack sufficient detail, making it difficult to detect them using a detector. To solve these problems a new traffic sign target detection algorithm is proposed, which introduces a new convolutional module SPD-Conv to capture the feature information of small target traffic signs more efficiently and replaces the C3 module in the backbone network with a C2f module, which greatly reduces the parameters and computation of the original network and achieves a lighter weight to make the model easier to deploy. A new weighted bi-directional feature pyramid network is used to replace the original feature pyramid network. In addition, a coordinate lightweight attention module is introduced for the small target feature loss problem, and finally, a new loss function is proposed to improve the convergence speed of the model while improving the small target detection accuracy. Extensive experimental results on CCTSDB and tt-100k datasets show that our method is more versatile and superior for traffic sign small target detection compared to several state-of-the-art methods.