SignParser: An End-to-End Framework for Traffic Sign Understanding

doi:10.1007/s11263-023-01912-9

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 模式分析与学习团队

	SignParser: An End-to-End Framework for Traffic Sign Understanding
	Guo, Yunfei 1,2; Feng, Wei 1,2; Yin, Fei 1,2; Liu, Cheng-Lin 1,2
刊名	INTERNATIONAL JOURNAL OF COMPUTER VISION
	2023-10-17
页码	17
关键词	Traffic sign understanding Content reasoning Semantic description generation
ISSN号	0920-5691
DOI	10.1007/s11263-023-01912-9
通讯作者	Guo, Yunfei(guoyunfei2019@ia.ac.cn)
英文摘要	In intelligent transportation systems, parsing traffic signs and transmitting traffic information to humans is an urgent need. However, despite the success achieved in the detection and recognition of low-level circular or triangular traffic signs, parsing the more complex and informative rectangular traffic signs remains unexplored and challenging. Our work is devoted to the topic called "Traffic Sign Understanding (TSU)", which is aimed to parse various traffic signs and generate semantic descriptions for them. To achieve this goal, we propose an end-to-end framework that integrates component detection, content reasoning, and semantic description generation. The component detection module first detects initial components in the sign image. Then the content reasoning module acquires the detailed content of the sign, including final components, their relations, and layout category, which provide local and global information for the subsequent module. In the end, the semantic description generation module mines relational attributes and text semantic attributes from the preceding results, embeds them with the layout categories, and transforms them into semantic descriptions through a dynamic prediction transformer. The three modules are trained jointly in an end-to-end manner for optimizing the overall performance. This method achieves state-of-the-art performance not only in the final semantic description generation stage but also on multiple subtasks of the CASIA-Tencent CTSU Dataset. Abundant ablation experiments are provided to prove the effectiveness of this method.
WOS关键词	NEURAL-NETWORK
WOS研究方向	Computer Science
语种	英语
出版者	SPRINGER
WOS记录号	WOS:001084915000001
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/54325]
专题	自动化研究所_模式识别国家重点实验室_模式分析与学习团队
通讯作者	Guo, Yunfei
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Guo, Yunfei,Feng, Wei,Yin, Fei,et al. SignParser: An End-to-End Framework for Traffic Sign Understanding[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION,2023:17.
APA	Guo, Yunfei,Feng, Wei,Yin, Fei,&Liu, Cheng-Lin.(2023).SignParser: An End-to-End Framework for Traffic Sign Understanding.INTERNATIONAL JOURNAL OF COMPUTER VISION,17.
MLA	Guo, Yunfei,et al."SignParser: An End-to-End Framework for Traffic Sign Understanding".INTERNATIONAL JOURNAL OF COMPUTER VISION (2023):17.