CTRE is an Iowa State University center, administered by the Institute for Transportation.

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Website: www.ctre.iastate.edu/

Iowa State University--Becoming the Best

Machine-Vision-Based Roadway Health Monitoring and Assessment

Researcher(s)

Principal investigators:

Co-principal investigators:

Project status

Completed

Start date: 05/15/15
End date: 01/31/16

Publications

Report: Machine-Vision-Based Roadway Health Monitoring and Assessment: Development of a Shape-Based Pavement-Crack-Detection Approach (2.80 mb pdf) January 2016

Tech transfer summary: Machine-Vision-Based Roadway Health Monitoring and Assessment: Development of a Shape-Based Pavement-Crack-Detection Approach tech transfer summary (380.46 kb pdf) Jan 2016

Sponsor(s)/partner(s)

Sponsor(s):

About the research

Abstract:

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.