Group multi-scale attention pyramid network for traffic sign detection

Traffic sign detection has made great progress with the rise of deep learning in recent years. As a result of the complex and changeable traffic environment, detecting small traffic signs in a real-world scene is still a challenging problem. In this paper, a novel group multi-scale attention pyramid network is proposed to address the problem. Specifically, to aggregate the feature at different scales and suppress the messy information in the background, an effective multi-scale attention module is proposed. Furthermore, a feature fusion module, named adaptive pyramid convolution, is further designed, which can drive the network to learn the optimal feature fusion pattern and construct an informative feature pyramid for detecting traffic signs in different sizes. Extensive experimental results on the public traffic sign detection datasets demonstrate the effectiveness and superiority of the proposed method when compared with several state-of-the-art methods. (c) 2021 Elsevier B.V. All rights reserved.