Traffic sign detection algorithm based on improved YOLOv4-Tiny

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. •A novel feature fusion method is proposed based on an Adaptive Feature Pyramid Network (AFPN). AFPN can better fuse the output results of the two scale feature layers of the backbone network, so the fused features have more semantic information and location information.•We proposed to add a receptive field block [16](RFB) after the output layer of the backbone network. RFBs use a multi-branch structure and a dilated convolution layer to superimpose different scale receptive fields and enhance the feature extraction ability of the convolutional neural network, thereby improving the detection accuracy of the network.•The experimental results show that compared with the original network (i.e., YOLOv4-tiny), the proposed network improves the precision, recall rate, and mAP by 2.62%, 2.17%, and 1.34%, respectively, on the CCTSDB dataset, and works better on the CCTSDB_s dataset with smaller traffic signs. At the same time, the frame processing rate of the proposed network is still up to 145.7 FPS.