Chinese Traffic Sign Recognition Based on Two-stage Classification Algorithm

Autonomous driving technology plays an important role in alleviating traffic congestion and reducing transportation cost. Advanced driver assistance systems (ADAS) can effectively increase the comfort and safety of car driving. Traffic signs contain rich semantic information, which provides important constraints for decision-making of autonomous vehicles and ADAS. Therefore, development of traffic sign recognition algorithms is very important. Based on the characteristics of traffic scenes in China and the high accuracy requirements of automatic driving and ADAS on traffic sign detection, this paper proposes a traffic sign recognition algorithm framework based on two-stage classification. The algorithm consists of two stages of recognition and classification. In the recognition stage, traffic signs in the image are detected. And in the classification stage, traffic signs are divided into categories and subcategories. By refining the task, the algorithm improves the performance of each algorithm module independently, thus improving the recognition accuracy of the whole algorithm. In this paper, the single-stage recognition algorithm is improved to be used as the recognition module of the algorithm. The experimental results show that the proposed algorithm is better than the benchmark single-stage recognition algorithm in accuracy, with an average increase of 8.52% in mAP. In addition, with the detection speed better than the traditional two-stage recognition algorithm Faster-RCNN, the mAP is improved by 40%. © 2022, Society of Automotive Engineers of China. All right reserved.