关键词: 交通标志检测, YOLOv3, GIoU, 维度聚类

Abstract:

Aiming at the problems of the large error rate, slow detection speed, and manual participation required for assisted identification of traffic signs in autonomous driving in China, an improved YOLOv3 traffic sign detection and recognition method is proposed. Firstly, the network structure of Darknet53 is improved to reduce the forward inference calculation during network iteration and increase the network iteration speed. Secondly, the direct evaluation index GIoU of target detection is introduced to guide the positioning task to improve the detection accuracy. Finally, the [k]-means++ clustering algorithm is used to obtain the anchor size and match the corresponding feature layer. The experimental results show that compared with the original YOLOv3, the proposed method has an average accuracy improvement of 8% on the standard data set Lisa, and the detection speed reaches 76.9 f/s. The average accuracy on the homemade data set CQ-data can reach 94.8%. Compared with traditional recognition and other algorithms, it not only has better real-time performance and accuracy, is more robust to various environmental changes, but also can recognize multiple types of traffic signs.

Key words: traffic sign detection, YOLOv3, GIoU, dimension clustering