1 April 2015 A Hessian-based methodology for automatic surface crack detection and classification from pavement images
Author Affiliations +
Around 3,000,000 million vehicle miles are annually traveled utilizing the US transportation systems alone. In addition to the road traffic safety, maintaining the road infrastructure in a sound condition promotes a more productive and competitive economy. Due to the significant amounts of financial and human resources required to detect surface cracks by visual inspection, detection of these surface defects are often delayed resulting in deferred maintenance operations. This paper introduces an automatic system for acquisition, detection, classification, and evaluation of pavement surface cracks by unsupervised analysis of images collected from a camera mounted on the rear of a moving vehicle. A Hessian-based multi-scale filter has been utilized to detect ridges in these images at various scales. Post-processing on the extracted features has been implemented to produce statistics of length, width, and area covered by cracks, which are crucial for roadway agencies to assess pavement quality. This process has been realized on three sets of roads with different pavement conditions in the city of Brockton, MA. A ground truth dataset labeled manually is made available to evaluate this algorithm and results rendered more than 90% segmentation accuracy demonstrating the feasibility of employing this approach at a larger scale.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sindhu Ghanta, Sindhu Ghanta, Salar Shahini Shamsabadi, Salar Shahini Shamsabadi, Jennifer Dy, Jennifer Dy, Ming Wang, Ming Wang, Ralf Birken, Ralf Birken, "A Hessian-based methodology for automatic surface crack detection and classification from pavement images", Proc. SPIE 9437, Structural Health Monitoring and Inspection of Advanced Materials, Aerospace, and Civil Infrastructure 2015, 94371Z (1 April 2015); doi: 10.1117/12.2084370; https://doi.org/10.1117/12.2084370

Back to Top