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.

翻译：对无人驾驶驾驶系统来说,交通标志的探测是一项艰巨的任务,特别是对于探测多级目标和实时探测问题而言。在交通标志检测过程中,目标的规模变化很大,这将对探测准确性产生一定影响。特质金字塔被广泛用于解决这一问题,但可能会打破不同交通标志规模的特征一致性。此外,在实际应用中,很难采用共同方法提高多级交通标志的检测准确性,同时确保实时检测。在本文中,我们建议采用改进的功能金字塔模型,名为AF-FPN,利用适应关注模块和功能增强模块(FEM),以减少特征地图生成过程中的信息损失,并提高特征金字塔的代表性能力。我们用AF-FPN取代了YOLOv5的原有特征金字塔网络,该网络在确保实时检测的前提下改进了YOLOv5网络多级目标的检测性能。此外,我们提议采用新的自动学习数据增强方法,以更新数据设置的适应性关注模块和功能增强模块的坚固性,使T-K的模型能更准确地展示“100-K”模型的实用性,从而更恰当地展示了“100-HT”的实验性结果。