Deep learning based approach for the instance segmentation of clayey soil desiccation cracks

Abstract

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 performances of clayey soils. Traditionally, the analysis of soil desiccation cracks relies on visual inspection and image processing techniques, which lack automation and intelligence. Therefore, there is an increasing need for an automated algorithm to meet accuracy and efficiency requirements for various engineering scenarios. In this study, a state-of-the-art deep-learning algorithm, Mask R-CNN, was utilized for the clayey soil crack detection, localization and segmentation. A comprehensive dataset including 1200 annotated crack images of 256 × 256 resolution was prepared for the algorithm training and validation. The proposed Mask R-CNN algorithm achieved precision, recall and F1 score of 73.29%, 82.76% and 77.74%, respectively. Besides, the algorithm gained a mean localization accuracy (APbb) of 64.14% and a mean segmentation accuracy (APm) of 47.59%. The detection performance of the Mask R-CNN was also compared with that of the U-Net under three different scenarios. The test results have demonstrated the superiority of the Mask R-CNN over the U-Net algorithm in crack detection, localization and segmentation.