Comparative analysis of infrared thermography processing techniques for roadways’ sub-pavement voids detection

Abstract

Roadways' sub-pavement voids caused by eroded soil through damaged culverts lead to safety hazards, traffic inconvenience, and expensive repairs. Infrared thermography (IRT) could help to identify those voids before the structural integrity of roadway pavements is compromised. However, IRT suffers from poor signal-to-noise ratio. This study implements the use of three advanced image-processing techniques to increase the accuracy of IRT in detecting voids underneath a roadway. A comparison between R2-based analysis, principal component thermography (PCT), and sparse principal component thermography (S-PCT) is presented and validated through extensive tests on a laboratory-scale section of a roadway. Results show pros and cons of the three techniques and how S-PCT allows determining the physical size of sub-pavement voids with an accuracy above 95%. This research provides the foundation for comparing advanced image-processing techniques that can progress the use of IRT as a more accurate and cost-effective nondestructive evaluation method for roadways’ condition monitoring.