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.

近年来，随着深度学习的兴起，交通标志检测取得了长足的进步。由于复杂多变的交通环境，在现实世界中检测小交通标志仍然是一个具有挑战性的问题。本文提出了一种新颖的群体多尺度注意力金字塔网络来解决这一问题。具体地，为了在不同尺度上聚合特征并在后台抑制杂乱信息，提出了一种有效的多尺度关注模块。此外，进一步设计了一个特征融合模块，称为自适应金字塔卷积，可以驱动网络学习最佳特征融合模式，并构建一个信息量特征金字塔，用于检测不同大小的交通标志。