6533b7d8fe1ef96bd1269901
RESEARCH PRODUCT
Hybrid Procedure for Automated Detection of Cracking with 3D Pavement Data
Joshua Qiang LiGiuseppe SollazzoGaetano BosurgiKelvin C. P. Wangsubject
EngineeringSpeedup0211 other engineering and technologies02 engineering and technologyMinimum spanning treeEdge (geometry)Minimum spanning treeThree-dimensional (3D) pavement dataTensor votingCrack detection; Matched filtering; Minimum spanning tree; Tensor voting; Three-dimensional (3D) pavement data; Civil and Structural Engineering; Computer Science Applications1707 Computer Vision and Pattern Recognition021105 building & construction0502 economics and businessThree dimensional dataSettore ICAR/04 - Strade Ferrovie Ed AeroportiCivil and Structural Engineering050210 logistics & transportationbusiness.industry05 social sciencesDetectorComputer Science Applications1707 Computer Vision and Pattern RecognitionStructural engineeringMatched filteringComputer Science ApplicationsCrackingCrack detectionTensor votingbusinessdescription
Pavement cracks are considered a major indicator of pavement performance. Because traditional manual crack surveys are dangerous, time consuming, and expensive, technologies have been developed to collect high-speed pavement images, and numerous algorithms have been proposed to detect cracks on pavement surface. The latest PaveVision3D Ultra system (3D Ultra) has been implemented to achieve 30-kHz three-dimensional (3D) scanning rate for 1-mm resolution pavement surface data at highway speed up to 100 km/h (60 mi/h). This paper presents the application of a hybrid procedure for automated crack detection on 3D pavement data collected using 3D Ultra. The procedure combines three different methods, namely, the matched filtering (MF) to highlight the cracks, the tensor voting to determine the main directions of the cracks, and the minimum spanning tree to identify the crack paths. The authors provide comparisons with cracking-detection results from traditional edge detectors and reference crack maps produced by a semiautomated software. The experimental results, through performance measurements and a statistical point of view, show that the proposed algorithm is able to detect pavement cracks with high precision. Finally, this paper also discusses preliminary considerations for exploiting the MF features to evaluate the orientation of the various crack segments.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-11-01 |