Automated subsurface defects' detection using point cloud reconstruction from infrared images

Abstract

Non-destructive evaluation (NDE) has become a reliable inspection tool to detect structural flaws in many engineering domains. Similarly, advancements in computer-vision made it possible to assess structural conditions from a set of digital images. This research introduces a novel inspection technique for detecting subsurface defects that cause heat loss. A combination of Structure from Motion (SfM) algorithms and infrared (IR) imaging has been developed for quantifying the severity of subsurface damages inducing energy loss on a scaled building structure. Furthermore, an automated detection algorithm is proposed to segment the contours of the damages. Results of experiments performed using different IR cameras prove that this approach can identify subsurface defects and quantify their dimensions with an error below 5% when compared to the actual size of the damages. The proposed approach can also be easily integrated with unmanned aerial vehicles for remote inspection and damage detection on large-scale systems.