中圖分類號：TP391
文獻標志碼：A
DOI: 10.16157/j.issn.0258-7998.222900
中文引用格式： 杜婷婷，鐘國韻，江金懋，等. 基于Darknet23和特征融合的交通標志檢測方法[J]. 電子技術應用，2023，49(1)：14-19.
英文引用格式： Du Tingting，Zhong Guoyun，Jiang Jinmao，et al. Traffic sign′s detection method based on Darknet23 and feature fusion[J]. Application of Electronic Technique，2023，49(1)：14-19.

Traffic sign′s detection method based on Darknet23 and feature fusion

Abstract： Road traffic sign′s detection is one of the important links of intelligent transportation. A detection method based on the improved YOLOv3 model by the industry is proposed for the problems of complex background, small targets and slow detection speed in traffic sign detection.The method used a bidirectional feature pyramid structure to achieve bidirectional fusion of semantic information of low,middle and high level features of images to improve the classification of low-level prediction targets and the localization of high-level prediction targets. The main feature extraction network of the original model is improved, and the Darknet23 network is proposed to improve the extraction ability of the network and reduce the computational burden.According to the characteristics of the target shape, the K-means clustering algorithm for training the appropriate anchor frames and a more flexible L_(α-CIOU) loss function is introduced into the bounding box regression to make the network optimize towards a higher degree of overlap between the prediction boxes and the ground-truth boxes. The experimental results show that the method reaches 86.10% mAP@0.75 and 70.017% mAP@0.5:0.05:0.95 on the CCTSDB dataset, which are 10.17% and 5.656% higher than the original network, the number of parameters is reduced by 3 622 091 and the speed is improved 8.27 f/s ,which is better than mainstream detection networks such as SSD and Faster RCNN.

Key words : traffic sign′s detection；bidirectional feature pyramid；Darknet23 network；K-means clustering；loss function