CCTSDB 2021: A more comprehensive traffic sign detection benchmark

Zhang, J., Zou, X., Kuang, L.-D., Wang, J., Sherratt, R. S. ORCID: https://orcid.org/0000-0001-7899-4445 and Yu, X. (2022) CCTSDB 2021: A more comprehensive traffic sign detection benchmark. Human-centric Computing and Information Sciences, 12. 23. ISSN 2192-1962 doi: 10.22967/HCIS.2022.12.023

Abstract/Summary

Traffic signs are one of the most important information that guide cars to travel, and the detection of traffic signs is an important component of autonomous driving and intelligent transportation systems. Constructing a traffic sign dataset with many samples and sufficient attribute categories will promote the development of traffic sign detection research. In this paper, we propose a new Chinese traffic sign detection benchmark, which adds more than 4,000 real traffic scene images and corresponding detailed annotations based on our CCTSDB 2017, and replaces many original easily-detected images with difficult samples to adapt to the complex and changing detection environment. Due to the increase of the number of difficult samples, the new benchmark can improve the robustness of the detection network to some extent compared to the old version. At the same time, we create new dedicated test sets and categorize them according to three aspects: category meanings, sign sizes, and weather conditions. Finally, we present a comprehensive evaluation of nine classic traffic sign detection algorithms on the new benchmark. Our proposed benchmark can help determine the future research direction of the algorithm and develop a more precise traffic sign detection algorithm with higher robustness and real-time performance.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/106129
Identification Number/DOI	10.22967/HCIS.2022.12.023
Refereed	Yes
Divisions	Life Sciences > School of Biological Sciences > Department of Bio-Engineering
Publisher	Springer Berlin Heidelberg
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