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.

裂缝破坏了交通基础设施的结构健康。基于机器视觉的表面裂纹分析是对成像设备采集的基础设施检测数据进行处理，以识别裂纹的存在、位置和程度，对相应的严重程度进行分类，并最终预测其增长情况。与现有研究中基于机器视觉的裂纹分析方法零碎的定性讨论不同，本文以细粒度的定量方式回顾了各种机器视觉解决方案在不同操作条件下的最新技术和实践，系统地描述了其优势和局限性。深度学习优于其他解决方案。此外，实施适用性评估以描述深度学习在五个裂缝分析任务中的部署和优化：图像分类、对象检测、像素分割、几何尺度量化和增长预测。最后，分别总结了面临的挑战和相应的突破方向，推动深度学习的进一步发展，以协助更复杂的维护决策。