Real-Time Traffic Sign Detection Algorithm Combining Attention Mechanism and Contextual Information

Abstract

Traffic sign detection has received widespread concern in recent years. However, existing methods often fail to meet the real-time detection requirements, and there are many cases of missing detection in small-scale traffic sign detection. To solve these problems, a real-time traffic sign detection algorithm combining attention mechanism and contextual information is proposed. Using YOLOv5 as the base model, firstly, spatial attention mechanism is embedded in the backbone to adaptively enhance the features of important positions and suppress interference information to improve the feature extraction capability of the backbone network. Secondly, the cross stage partial window Transformer module is designed to learn correlations of different locations and to capture rich contextual information around traffic signs, which is beneficial to improving the detection accuracy of small-scale traffic signs. Thirdly, the lightweight feature fusion network is proposed to fuse the feature maps of different scales, which can reduce the computational burden and ensure the effective feature fusion. Finally, in the post-processing stage,  Gaussian weighted fusion is used to amend the prediction boxes to improve the positioning accuracy. Experiments on TT100K and DFG traffic sign detection datasets show that the proposed method can effectively improve the missing detection of small-scale traffic signs, with higher accuracy and real-time performance, and can meet the requirements of traffic sign detection in actual scenarios.

Keywords

• traffic sign detection; yolov5; attention mechanism; transformer; contextual information