Research on Intelligent Detection and Segmentation of Rock Joints Based on Deep Learning

The current methods for detecting joints on tunnel face rely primarily on manual sketches, which are associated with issues of low detection efficiency and subjectivity. To address these concerns, this paper presents an intelligent recognition and segmentation algorithm based on Mask R-CNN (mask region-based convolutional neural network) for detecting joint targets on tunnel face images and automatically segmenting them, thereby improving detection efficiency and objectivity of the results. Additionally, to tackle the challenge of low detection accuracy in existing image processing methods, particularly for complex tunnel joint surfaces in dark environments, the paper introduces a path aggregation network (PANet) to enhance the fusion capability of feature information in Mask R-CNN, thereby improving the accuracy of the intelligent detection method. The algorithm was trained on a dataset of 800 tunnel face images, and the research findings demonstrate that it can quickly detect the position of joints on tunnel face images and assign masks to the joint pixel regions to achieve joint segmentation. The mean average precision (mAP) of the detection boxes and segmentation in the 80 test set images were 58.0% and 49.2%, respectively, which outperforms the original Mask R-CNN algorithm and other intelligent recognition and segmentation algorithms.