Machine vision-based surface crack analysis for transportation infrastructure

Cracks undermine the structural health of transportation infrastructure. Machine vision-based surface crack analysis is to process infrastructure inspection data collected by imaging devices for identifying the presence, location, and extent of cracks, classifying the corresponding severity levels, and eventually predicting their growth. Unlike the fragmented qualitative discussions on machine vision-based crack analysis methods in existing studies, this paper reviews the state of the art and practice of various machine vision solutions under different operating conditions in a fine-grained quantitative way, systematically describing the strengths and limitations of deep learning over other solutions. Moreover, the applicability assessment is implemented to describe the deployment and optimization of deep learning in five crack analysis tasks: image classification, object detection, pixel segmentation, geometric scale quantification, and growth prediction. At last, the challenges faced and corresponding breakthrough directions are summarized, respectively, driving further development of deep learning to assist more sophisticated maintenance decisions.