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Article

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

1
College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China
2
State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(15), 4198; https://doi.org/10.3390/s20154198
Received: 1 July 2020 / Revised: 15 July 2020 / Accepted: 26 July 2020 / Published: 28 July 2020
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. The F1 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. View Full-Text
Keywords: mobile laser scanning; pavement cracks; crack shape parameters mobile laser scanning; pavement cracks; crack shape parameters
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MDPI and ACS Style

Zhong, M.; Sui, L.; Wang, Z.; Hu, D. Pavement Crack Detection from Mobile Laser Scanning Point Clouds Using a Time Grid. Sensors 2020, 20, 4198. https://doi.org/10.3390/s20154198

AMA Style

Zhong M, Sui L, Wang Z, Hu D. Pavement Crack Detection from Mobile Laser Scanning Point Clouds Using a Time Grid. Sensors. 2020; 20(15):4198. https://doi.org/10.3390/s20154198

Chicago/Turabian Style

Zhong, Mianqing, Lichun Sui, Zhihua Wang, and Dongming Hu. 2020. "Pavement Crack Detection from Mobile Laser Scanning Point Clouds Using a Time Grid" Sensors 20, no. 15: 4198. https://doi.org/10.3390/s20154198

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