Learning Region-Based Attention Network for Traffic Sign Recognition

Sensors

Ke Zhou¹  Yufei Zhan²  Dongmei Fu³ 

[1] Collaborative Innovation Center of Steel Technology, University of Science and Technology, Beijing 100083, China;School of Advanced Engineering, University of Science and Technology, Beijing 100083, China;School of Automation and Electrical Engineering, University of Science and Technology, Beijing 100083, China;

关键词: traffic sign classification;    attention;    region-based;    ice environment;    ice traffic sign;    recognition benchmark;   

DOI  :  10.3390/s21030686

来源: DOAJ

【 摘 要 】

Traffic sign recognition in poor environments has always been a challenge in self-driving. Although a few works have achieved good results in the field of traffic sign recognition, there is currently a lack of traffic sign benchmarks containing many complex factors and a robust network. In this paper, we propose an ice environment traffic sign recognition benchmark (ITSRB) and detection benchmark (ITSDB), marked in the COCO2017 format. The benchmarks include 5806 images with 43,290 traffic sign instances with different climate, light, time, and occlusion conditions. Second, we tested the robustness of the Libra-RCNN and HRNetv2p on the ITSDB compared with Faster-RCNN. The Libra-RCNN performed well and proved that our ITSDB dataset did increase the challenge in this task. Third, we propose an attention network based on high-resolution traffic sign classification (PFANet), and conduct ablation research on the design parallel fusion attention module. Experiments show that our representation reached 93.57% accuracy in ITSRB, and performed as well as the newest and most effective networks in the German traffic sign recognition dataset (GTSRB).

【 授权许可】

Unknown