Machine-Vision-Based Roadway Health Monitoring and Assessment: Development of a Shape-Based Pavement-Crack-Detection Approach

dc.contributor.author Ceylan, Halil
dc.contributor.author Smadi, Omar
dc.contributor.author Gopalakrishnan, Kasthurirangan
dc.contributor.author Celik, Koray
dc.contributor.author Somani, Arun
dc.contributor.department Institute for Transportation
dc.date 2018-02-17T15:43:03.000
dc.date.accessioned 2020-06-30T04:51:38Z
dc.date.available 2020-06-30T04:51:38Z
dc.date.embargo 2016-04-05
dc.date.issued 2016-01-01
dc.description.abstract <p>State highway agencies (SHAs) routinely employ semi-automated and automated image-based methods for network-level pavement-cracking data collection, and there are different types of pavement-cracking data collected by SHAs for reporting and management purposes. The main objective of this proof-of-concept research was to develop a shape-based pavement-crack-detection approach for the reliable detection and classification of cracks from acquired two-dimensional (2D) concrete and asphalt pavement surface images. The developed pavement-crack-detection algorithm consists of four stages: local filtering, maximum component extraction, polynomial fitting of possible crack pixels, and shape metric computation and filtering. After completing the crack-detection process, the width of each crack segment is computed to classify the cracks. In order to verify the developed crack-detection approach, a series of experiments was conducted on real pavement images without and with cracks at different severities. The developed shape-based pavement crack detection algorithm was able to detect cracks at different severities from both asphalt and concrete pavement images. Further, the developed algorithm was able to compute crack widths from the images for crack classification and reporting purposes. Additional research is needed to improve the robustness and accuracy of the developed approach in the presence of anomalies and other surface irregularities.</p>
dc.description.comments <p>For additional information or reports on other topics, please go to the InTrans research website: http://www.intrans.iastate.edu/research/projects/detail/?projectID=72765752</p>
dc.format.mimetype pdf
dc.identifier archive/lib.dr.iastate.edu/intrans_techtransfer/102/
dc.identifier.articleid 1101
dc.identifier.contextkey 8428313
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath intrans_techtransfer/102
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/44947
dc.language.iso English
dc.source.bitstream archive/lib.dr.iastate.edu/intrans_techtransfer/102/machine_vision_based_roadway_health_t2.pdf|||Fri Jan 14 18:15:51 UTC 2022
dc.subject.disciplines Civil Engineering
dc.subject.keywords asphalt concrete pavements
dc.subject.keywords asphalt pavement condition
dc.subject.keywords concrete pavement condition
dc.subject.keywords image-based crack detection
dc.subject.keywords pavement condition monitoring
dc.subject.keywords pavement cracking data
dc.subject.keywords shape-based algorithm
dc.title Machine-Vision-Based Roadway Health Monitoring and Assessment: Development of a Shape-Based Pavement-Crack-Detection Approach
dc.type article
dc.type.genre report
dspace.entity.type Publication
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relation.isOrgUnitOfPublication 0cffd73a-b46d-4816-85f3-0f6ab7d2beb8
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