Crack Detection Using Deep Learning and Confidence in Width Estimation

Abstract

Automatic detection of cracks using deep learning has been studied as one of the measures to cope with the aging of concrete structures, which is currently a problem throughout Japan. In addition, research is also being conducted on width estimation to measure the degree of damage of cracks to assist inspection work. In this study, we used probability maps of cracks obtained from deep learning to estimate widths with higher accuracy and to calculate confidence levels corresponding to the estimated widths. The statistical analysisof the error between the estimated width and the correct width in 17 images for evaluation showed the relationship between the confidence level and the error of the estimated width and confirmed the validity of the results.