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Surface cracks are a very common indicator of potential structural faults.Their early detection and monitoring is an important factor in structuralhealth monitoring. Left untreated, they can grow in size over time and requireexpensive repairs or maintenance. With recent advances in computer vision anddeep learning algorithms, the automatic detection and segmentation of cracksfor this monitoring process have become a major topic of interest. This reviewaims to give researchers an overview of the published work within the field ofcrack analysis algorithms that make use of deep learning. It outlines thevarious tasks that are solved through applying computer vision algorithms tosurface cracks in a structural health monitoring setting and also providesin-depth reviews of recent fully, semi and unsupervised approaches that performcrack classification, detection, segmentation and quantification. Additionally,this review also highlights popular datasets used for cracks and the metricsthat are used to evaluate the performance of those algorithms. Finally,potential research gaps are outlined and further research directions areprovided.

What's Cracking? A Review and Analysis of Deep Learning Methods for Structural Crack Segmentation, Detection and Quantification