MTC research brief: Machine-Vision-Based Roadway Health Monitoring and Assessment

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July 06, 2016

The Midwest Transportation Center (MTC) sponsors a competitive research program to fund projects focused on State of Good Repair in infrastructure with attention to safety and Data Driven Performance Measures for Enhanced Infrastructure Condition.

The following details one project led by Iowa State University. Stay up to date on research conducted by the MTC here.

 

Iowa State University

Machine-Vision-Based Roadway Health Monitoring and Assessment

Project PI: Kasthurira Gopalakrishnan                

Project Co-PIs: Omar Smadi, Halil Ceylan, Koray Celik, and Arun Somani   

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.

Some key findings of this research included the following:

  • The shape-based pavement-crack-detection algorithm was able to detect cracks at different severities in both asphalt and concrete pavement images, although some partial misses were observed.
  • The algorithm was able to compute crack widths from the images for crack classification and reporting purposes.

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