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Open AccessArticle

Learning Region-Based Attention Network for Traffic Sign Recognition

by 1, 2 and 3,*
1
Collaborative Innovation Center of Steel Technology, University of Science and Technology, Beijing 100083, China
2
School of Advanced Engineering, University of Science and Technology, Beijing 100083, China
3
School of Automation and Electrical Engineering, University of Science and Technology, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(3), 686; https://doi.org/10.3390/s21030686
Received: 7 December 2020 / Revised: 17 January 2021 / Accepted: 18 January 2021 / Published: 20 January 2021
(This article belongs to the Section Intelligent Sensors)
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). View Full-Text
Keywords: traffic sign classification; attention; region-based; ice environment; ice traffic sign; recognition benchmark; ice traffic sign detection benchmark traffic sign classification; attention; region-based; ice environment; ice traffic sign; recognition benchmark; ice traffic sign detection benchmark
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MDPI and ACS Style

Zhou, K.; Zhan, Y.; Fu, D. Learning Region-Based Attention Network for Traffic Sign Recognition. Sensors 2021, 21, 686. https://doi.org/10.3390/s21030686

AMA Style

Zhou K, Zhan Y, Fu D. Learning Region-Based Attention Network for Traffic Sign Recognition. Sensors. 2021; 21(3):686. https://doi.org/10.3390/s21030686

Chicago/Turabian Style

Zhou, Ke; Zhan, Yufei; Fu, Dongmei. 2021. "Learning Region-Based Attention Network for Traffic Sign Recognition" Sensors 21, no. 3: 686. https://doi.org/10.3390/s21030686

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