  Traffic sign detection is a challenging task for the unmanned driving system,
especially for the detection of multi-scale targets and the real-time problem
of detection. In the traffic sign detection process, the scale of the targets
changes greatly, which will have a certain impact on the detection accuracy.
Feature pyramid is widely used to solve this problem but it might break the
feature consistency across different scales of traffic signs. Moreover, in
practical application, it is difficult for common methods to improve the
detection accuracy of multi-scale traffic signs while ensuring real-time
detection. In this paper, we propose an improved feature pyramid model, named
AF-FPN, which utilizes the adaptive attention module (AAM) and feature
enhancement module (FEM) to reduce the information loss in the process of
feature map generation and enhance the representation ability of the feature
pyramid. We replaced the original feature pyramid network in YOLOv5 with
AF-FPN, which improves the detection performance for multi-scale targets of the
YOLOv5 network under the premise of ensuring real-time detection. Furthermore,
a new automatic learning data augmentation method is proposed to enrich the
dataset and improve the robustness of the model to make it more suitable for
practical scenarios. Extensive experimental results on the Tsinghua-Tencent
100K (TT100K) dataset demonstrate the effectiveness and superiority of the
proposed method when compared with several state-of-the-art methods.
