Extraction and Orientation Analysis of Rock Mass Discontinuities Using UAV-Assisted Photogrammetry

The stability of rock masses is crucial for the safety of hydraulic engineering, as the integrity of the rock mass directly influences the stability of structures such as dams, reservoirs, and tunnels. Accurate extraction and orientation of rock mass discontinuities plays a key role in stability analysis, providing essential geometric data for assessing rock mass behavior. However, traditional manual measurement methods used to extract these orientations are not only time-consuming and labor-intensive but also fraught with safety risks, especially when working on large and steep slopes. These limitations hinder the efficiency and accuracy of rock mass stability assessments.

To address these challenges, this paper proposes a novel approach for acquiring 3D rock mass scenes using unmanned aerial vehicles (UAVs), coupled with oblique photogrammetry technology for 3D scene reconstruction. With UAVs equipped with high-resolution cameras to capture image sequences from various angles, the Structure from Motion (SfM) algorithm is then applied to reconstruct the 3D scene. This method allows for the generation of high-precision point cloud data through geometric uniform sampling, ensuring accurate representation of rock mass. Once the 3D scene is reconstructed, local geometric features (including surface curvature, planarity, scattering, and verticality) are calculated based on neighborhood search. Combined with RGB texture information, machine learning method is employed to analyze the importance of these features, and further identify and differentiate rock mass features from vegetation and outliers within the large-scale slope scene, followed by a region-growing and merging algorithm for the segmentation of rock mass patches. For each individual patch, a local planar coordinate system is established to generate a grayscale image, which is then used for edge detection to identify structural boundaries. Following this, line extraction is carried out using an energy-optimization-based graph cut algorithm, and the closed contours of the structural patches are delineated through vectorization, ensuring an accurate and detailed mapping of the rock mass structure.

The effectiveness of the proposed method was validated through experiments conducted on a large-scale rock mass slope scene. The results demonstrate that the method can accurately extract the rock mass structural regions, identify the fracture network, and provide crucial geometric features, such as dip, strike, and trace information for each structural plane. The extracted features significantly contribute to evaluating the structural integrity and stability of large-scale slopes, offering a more efficient, accurate, and safer alternative to traditional manual measurement methods. Moreover, this method can be applied to a wide range of geological environments, providing a valuable tool for real-time monitoring and assessment in engineering projects.