Multi-CrackNet: A Fast Segmentation and Quantification Combined Method for Rock Discontinuity Traces by Capturing an Image

Discontinuity investigation and characterization onsite is a labor-dependent work because current techniques cannot precisely handle multiple discontinuity identifications automatically under different work conditions. This paper proposes the multi-CrackNet which enables us to identify and segment linear discontinuities (joints and cracks) for random types of rock surface. A modified feature extraction network called the multiscale feature fusion pyramid network (MFFPN) has been developed based on FPN to capture and fuse more sensitive texture features of cracks across different types of background. With the help of a new training scheme by setting up 3 stages of training to simulate the human-based learning process, the established model can learn more features steadily and robustly from well-labelled databases. Additionally, a hybrid pixel-level quantification method is proposed to automatically compute the length, width, and inclination of cracks. Results show that the proposed method can achieve a detection accuracy of 87.1% for 1 to 9 sets of cracks on the rock surface across different types of rock. Case studies in Anshan West are provided to verify the reliability and accuracy of our method in macrolinear discontinuity identification and quantification, which sees great potentials in site investigation by saving a large amount of labor force.