The identification of clayey soil desiccation cracks is an important practical issue in geotechnical engineering and engineering geology. The desiccation cracks can dramatically increase the hydraulic conductivity and deteriorate the mechanical performance of clayey soils. Traditionally, the analysis of soil desiccation cracks relies on visual inspection and image processing techniques, which lacks automation and intelligence. Therefore, there is an increasing need for an automated algorithm that could meet accuracy and efficiency requirements for various engineering scenarios. In this study, a algorithm developed based on a state-of-the-art deep-learning algorithm, Mask R-CNN, was utilized for the clayey soil crack detection, locating, segmentation and statistics.
