Utilizing semantic-level computer vision for fracture trace characterization of hard rock pillars in underground space

This study utilizes a semantic-level computer vision-based detection to characterize fracture traces of hard rock pillars in underground space. The trace images captured by photogrammetry are used to establish the database for training two convolutional neural network (CNN)-based models, i.e., U-Net (University of Freiburg, Germany) and DeepLabV3+ (Google, USA) models. Chain code technology, polyline approximation algorithm, and the circular window scanning approach are combined to quantify the main characteristics of fracture traces on flat and uneven surfaces, including trace length, dip angle, density, and intensity. The extraction results indicate that the CNN-based models have better performances than the edge detection methods-based Canny and Sobel operators for extracting the trace and reducing noise, especially the DeepLabV3+ model. Furthermore, the quantization results further prove the reliability of extracting the fracture trace. As a result, a case study with two types of traces (i.e., on flat and uneven surfaces) demonstrates that the applied semantic-level computer vision detection is an accurate and efficient approach for characterizing the fracture trace of hard rock pillars.