There are three problems in YOLOv4-Tiny when it is used for traffic sign detection: the feature pyramid network fails to fuse high-level and low-level features sufficiently, the importance of low-level features for small object detection is not considered, and the ability to extract the features of small objects in the backbone network is not strong. Focusing on these problems, this paper proposes an improved YOLOv4 -Tiny for real-time traffic sign detection. Firstly, this paper improves YOLOv4-Tiny’s feature fusion method and proposes an adaptive feature pyramid network (AFPN), which aims to adaptively fuse the two feature layers with different scales. Secondly, two receptive field blocks (RFB) are added after the two feature layers of the backbone network. These two RFBs are composed of multi-branch structures and dilated convolution with different dilation rates, which can enhance the feature extraction ability of the backbone network. The CCTSDB and GTSDB datasets are used to evaluate the effectiveness of the improved method. The experimental results show that our proposed network is superior to the original network in the precision, recall rate, and mAP. In addition, compared with other state-of-the-art approaches on traffic sign detection, our proposed network has good comprehensive performance in accuracy and speed. The above results show that our improved method is effective in improving the performance of traffic sign detection.

YOLOv4-Tiny用于交通标志检测存在三个问题：特征金字塔网络未能充分融合高层和低层特征，未考虑低层特征对小目标检测的重要性，以及在主干网络中提取小物体特征的能力不强。针对这些问题，本文提出了一种改进的YOLOv4 -Tiny，用于实时交通标志检测。首先，本文改进了YOLOv4-Tiny的特征融合方法，提出了一种自适应特征金字塔网络（AFPN），旨在自适应地融合不同尺度的两个特征层。其次，在主干网络的两个特征层之后添加两个感受野块（RFB）。这两个RFB由多分支结构和不同扩张率的扩张卷积组成，可以增强骨干网络的特征提取能力。CCTSDB 和 GTSDB 数据集用于评估改进方法的有效性。实验结果表明，我们提出的网络在精度、召回率和mAP上均优于原始网络。此外，与其他最先进的交通标志检测方法相比，我们提出的网络在准确性和速度方面具有良好的综合性能。上述结果表明，我们改进的方法在提高交通标志检测性能方面是有效的。CCTSDB 和 GTSDB 数据集用于评估改进方法的有效性。实验结果表明，我们提出的网络在精度、召回率和mAP上均优于原始网络。此外，与其他最先进的交通标志检测方法相比，我们提出的网络在准确性和速度方面具有良好的综合性能。上述结果表明，我们改进的方法在提高交通标志检测性能方面是有效的。CCTSDB 和 GTSDB 数据集用于评估改进方法的有效性。实验结果表明，我们提出的网络在精度、召回率和mAP上均优于原始网络。此外，与其他最先进的交通标志检测方法相比，我们提出的网络在准确性和速度方面具有良好的综合性能。上述结果表明，我们改进的方法在提高交通标志检测性能方面是有效的。