OCM: an intelligent recognition method of rock discontinuity based on optimal color mapping of 3D Point cloud via deep learning

Discontinuities largely influence the mechanical properties of rock joints. However, traditional discontinuity recognition methods often require manual intervention during processing. This paper proposes a new deep-learning-based method for discontinuity recognition using 3D point clouds. A neighborhood PCA-weighted oriented contraction method is proposed to extract point cloud skeletons as discontinuity intersection lines. Then an optimal color mapping (OCM) method is proposed to establish the optimal mapping relationship between 3D normal vectors and RGB, converting 3D point clouds to 2D OCM images for discontinuity segmentation. The convolutional neural network of Mask R-CNN is adopted to segment discontinuities from OCM images. Finally, 3D discontinuities can be generated from discontinuity-segmented OCM images. Forty-two rock slope image sequences and a rock slope point cloud are collected and labeled, generating 4632 OCM images including 430,613 discontinuity planes after data augmentation for training. Three cases of rock slopes and rock tunnel excavation faces are adopted for testing. The average recognition time per 3D point cloud model is approximately 12 s due to the high recognition efficiency of Mask R-CNN for 2D images. The results show the proposed method can recognize discontinuities close to manual judgements with high accuracy, good robustness to point cloud density variations, and good adaptability to different rock engineering scenarios. Highlights An NPW-OC method is proposed to extract point cloud skeletons. An OCM method is proposed to assign 3D normal vectors with optimal RGB. OCM images are generated to assign discontinuities with different and uniform color. Deep-learning-based method is used for intelligent recognition of discontinuities. Conversion of discontinuity recognition from 3D point clouds to 2D OCM images. The results show good efficiency, accuracy, robustness, and adaptability .