Refined Crack Detection via LECSFormer for Autonomous Road Inspection Vehicles

doi:10.1109/TIV.2022.3204583

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	Refined Crack Detection via LECSFormer for Autonomous Road Inspection Vehicles
	Chen, Junzhou 3,5; Zhao, Nan 3,5; Zhang, Ronghui 3,5; Chen, Long 1; Huang, Kai 2; Qiu, Zhijun 4
刊名	IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
	2023-03-01
卷号	8 期号:3 页码:2049-2061
关键词	Roads Transformers Feature extraction Diseases Inspection Shape Intelligent vehicles Refined crack detection window-based trans-former semantic segmentation autonomous road inspection vehicles
ISSN号	2379-8858
DOI	10.1109/TIV.2022.3204583
通讯作者	Zhang, Ronghui(zhangrh25@mail.sysu.edu.cn)
英文摘要	Due to the rising cost of human resources in road maintenance and the pursuit of efficiency, autonomous road inspection vehicles are developed for intelligent detection of road disease to prevent severe traffic disasters in the early stages. Nevertheless, as a prevalent road disease, road cracks are diverse and susceptible to shadows, weather changes, and noise in data acquisition. Moreover, they usually appear with thin shapes that are hard to detect correctly by existing methods. To handle this problem, more details of the road cracks need to be better analyzed. In this article, we propose a refined road crack detection method named locally enhanced cross-shaped windows transformer (LECSFormer), which adopts a delicate design of the encoder-decoder structure. The encoder employs window-based transformer blocks to model long-range dependencies. Each transformer block ensembles the locally enhanced module to enrich the local contextual information, and token shuffle operation is applied to build cross-windows connections. The decoder uses dense connections to fuse multi-scale information, and the feature fusion module fuses hierarchical features and reweights them by the channel attention mechanism. The proposed method outperforms other state-of-the-art methods with ODS of 0.963, 0.917, 0.952, and 0.953 on four challenging datasets, CrackTree260, CrackLS315, Stone331, and CRKWH100. It can accurately detect cracks in road surfaces and support intelligent preventive maintenance of roads.
资助项目	Shenzhen Key Science and Technology Program[JSGG20210802153412036] ; Shenzhen Fundamental Research Program[JCYJ20200109142217397] ; Guangdong Natural Science Foundation[2021A1515011794] ; Guangdong Natural Science Foundation[2021B1515120032] ; National Natural Science Foundation of China[52172350] ; National Natural Science Foundation of China[51775565] ; Guangzhou Science and Technology Plan Project[202206030005] ; Guangzhou Science and Technology Plan Project[202007050004]
WOS关键词	DAMAGE DETECTION ; NEURAL-NETWORK ; SEGMENTATION
WOS研究方向	Computer Science ; Engineering ; Transportation
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000981348100006
资助机构	Shenzhen Key Science and Technology Program ; Shenzhen Fundamental Research Program ; Guangdong Natural Science Foundation ; National Natural Science Foundation of China ; Guangzhou Science and Technology Plan Project
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/53320]
专题	多模态人工智能系统全国重点实验室
通讯作者	Zhang, Ronghui
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Sun Yat sen Univ, Sch Data & Comp Sci, Guangzhou 510275, Peoples R China 3.Sun Yat sen Univ, Sch Intelligent Syst Engn, Shenzhen Campus, Shenzhen 518107, Guangdong, Peoples R China 4.Univ Alberta, Dept Civil & Environm Engn, Edmonton, AB T6G 2R3, Canada 5.Sun Yat sen Univ, Guangdong Prov Key Lab Fire Sci & Intelligent Emer, Guangzhou 510006, Peoples R China
推荐引用方式 GB/T 7714	Chen, Junzhou,Zhao, Nan,Zhang, Ronghui,et al. Refined Crack Detection via LECSFormer for Autonomous Road Inspection Vehicles[J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,2023,8(3):2049-2061.
APA	Chen, Junzhou,Zhao, Nan,Zhang, Ronghui,Chen, Long,Huang, Kai,&Qiu, Zhijun.(2023).Refined Crack Detection via LECSFormer for Autonomous Road Inspection Vehicles.IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,8(3),2049-2061.
MLA	Chen, Junzhou,et al."Refined Crack Detection via LECSFormer for Autonomous Road Inspection Vehicles".IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 8.3(2023):2049-2061.