Automatic detection and damage quantification of multiple cracks on concrete surface from video

Real-time automatic detection of multiple cracks from a video stream of a concrete surface is addressed in this paper. Robust principal component analysis is used to detect multiple cracks forming at different instances of time in an unsupervised manner using the Gini index as a metric to quantify the presence of an observable crack. The relative positions of the relevant pixels around the crack are monitored using the Kanade Lucas Tomasi feature tracking algorithm. Further, Hu's invariant moments of those pixel positions are computed which acts as a robust damage indicator even for breathing cracks under time-varying service loads. The proposed method is experimentally validated using two small scale under-reinforced beams undergoing three-point bending tests. The method successfully detects the onset of multiple cracks, at varied locations, at different time instants and further tracks their propagations.