Traffic sign detection algorithm based on improved YOLOv4-Tiny
作者:
Highlights:
• 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.
摘要
•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.
论文关键词:Traffic sign detection,YOLOv4-tiny,Small object,Adaptive feature pyramid network,Receptive field block
论文评审过程:Received 2 June 2021, Revised 16 March 2022, Accepted 13 June 2022, Available online 16 June 2022, Version of Record 29 June 2022.
论文官网地址:https://doi.org/10.1016/j.image.2022.116783