CoreDeep: Improving Crack Detection Algorithms Using Width Stochasticity

Automatically detecting or segmenting cracks in images can help in reducing the cost of maintenance or operations. Detecting, measuring and quantifying cracks for distress analysis in challenging background scenarios is a difficult task as there is no clear boundary that separates cracks from the background. Some of the other perceptually noted challenges with the images are variations in color, intensity, depth, blur, motion-blur, orientation, region of interest (ROI) for the defect, scale, illumination, backgrounds, etc. These variations occur across (crack inter-class) and within images (crack intra-class variabilities). In this work, we have proposed a stochastic width (SW) approach to reduce the effect of these variations. Our proposed approach improves detectability and significantly reduces false positives and negatives. We have measured the performance of our algorithm objectively in terms of mean intersection over union (mIoU) and subjectively in terms of perceptual quality.