Pavement Crack Detection from Mobile Laser Scanning Point Clouds Using a Time Grid

doi:10.3390/s20154198

	Pavement Crack Detection from Mobile Laser Scanning Point Clouds Using a Time Grid
	Zhong, Mianqing 1; Sui, Lichun 1; Wang, Zhihua 2; Hu, Dongming 1
刊名	SENSORS
	2020-08-01
卷号	20 期号:15 页码:20
关键词	mobile laser scanning pavement cracks crack shape parameters
DOI	10.3390/s20154198
通讯作者	Sui, Lichun(sui1011@chd.edu.cn)
英文摘要	This paper presents a novel algorithm for detecting pavement cracks from mobile laser scanning (MLS) data. The algorithm losslessly transforms MLS data into a regular grid structure to adopt the proven image-based methods of crack extraction. To address the problem of lacking topology, this study assigns a two-dimensional index for each laser point depending on its scanning angle or acquisition time. Next, crack candidates are identified by integrating the differential intensity and height changes from their neighbors. Then, morphology filtering, a thinning algorithm, and the Freeman codes serve for the extraction of the edge and skeleton of the crack curves. Further than the other studies, this work quantitatively evaluates crack shape parameters: crack direction, width, length, and area, from the extracted crack points. TheF1 scores of the quantity of the transverse, longitudinal, and oblique cracks correctly extracted from the test data reached 96.55%, 87.09%, and 81.48%, respectively. In addition, the average accuracy of the crack width and length exceeded 0.812 and 0.897. Experimental results demonstrate that the proposed approach is robust for detecting pavement cracks in a complex road surface status. The proposed method is also promising in serving the extraction of other on-road objects.
资助项目	National Natural Science Foundation of China[41890854] ; National Natural Science Foundation of China[41372330] ; Transportation Research Project of Shaanxi Provincial Transport Department (SPTD)[16-01K] ; Transportation Research Project of Shaanxi Provincial Transport Department (SPTD)[18-06K] ; Fundamental Research Funds for the Central Universities[CHD 300102269304]
WOS关键词	CONVOLUTIONAL NEURAL-NETWORKS ; ASPHALT PAVEMENT ; EXTRACTION
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
语种	英语
出版者	MDPI
WOS记录号	WOS:000567245600001
资助机构	National Natural Science Foundation of China ; Transportation Research Project of Shaanxi Provincial Transport Department (SPTD) ; Fundamental Research Funds for the Central Universities
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/156831]
专题	中国科学院地理科学与资源研究所
通讯作者	Sui, Lichun
作者单位	1.Changan Univ, Coll Geol Engn & Geomat, Xian 710054, Peoples R China 2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Zhong, Mianqing,Sui, Lichun,Wang, Zhihua,et al. Pavement Crack Detection from Mobile Laser Scanning Point Clouds Using a Time Grid[J]. SENSORS,2020,20(15):20.
APA	Zhong, Mianqing,Sui, Lichun,Wang, Zhihua,&Hu, Dongming.(2020).Pavement Crack Detection from Mobile Laser Scanning Point Clouds Using a Time Grid.SENSORS,20(15),20.
MLA	Zhong, Mianqing,et al."Pavement Crack Detection from Mobile Laser Scanning Point Clouds Using a Time Grid".SENSORS 20.15(2020):20.