# Improved FraSegNet-Based Rock Nodule Identification Method and Application
## 1 Introduction  
Rock joints are critical structural features in geological engineering, significantly influencing the stability and mechanical behavior of rock masses. The shape, position, and strength of joints are crucial for building tunnels, slopes, mines, and water projects. Understanding these features is essential for predicting rock mass behavior under various engineering conditions. However, accurately identifying and characterizing rock joints pose significant challenges due to their complex geometries, varying scales, and the influence of environmental factors. In mining engineering, the development of rock joints directly affects the stability of the excavation face and the reliability of the support system. Joints are natural fissures in the rock body, and their distribution, density, directionality, and filler properties determine the mechanical behavior of the rock body. It is of great significance to study the influence of joints on mining support fixation and roadway excavation stability to ensure safe andefficient mining. Rock joints have an important influence on mining support fixation and roadway excavation stability, which is mainly reflected in the following aspects: (1) The existence of joints makes the rock body become a discontinuous medium, resulting in the overall strength of the rock body decreasing, the stiffness decreasing, and presenting obvious anisotropy. In the excavation process, the joints may become a potential slip surface or damage interface, which makes the rock body prone to structural instability, such as block slip, wedge collapse, cleavage damage, and so on. When the direction of joints and the main stress direction or intersection angle is small, it is easy to form an unstable block, threatening the safety of the pit and roadway. (2) The rock body with developed joints usually needs a more perfect support system to improve the stability of the excavation face. Common support methods include anchor rods, anchor cables, sprayed concrete, steel arches, and grouting reinforcement. In weak rock bodies with developed joints, a single support measure may be difficult to provide sufficient stability, and a combination of reinforcement means is required. Meanwhile, grouting reinforcement can be used to fill the joints and improve the shear strength and bearing capacity of the rock mass. (3) In order to reduce the impact of joints on excavation stability, a detailed geological survey should be carried out at the mine design stage, and techniques such as geo-radar, 3D laser scanning, and deep learning image analysis should be used to identify the distribution characteristics of joints, and numerical simulation should be combined to predict the stability of the rock body. In the excavation process, step-by-step excavation, step method, and circular excavation can be used to avoid the one-time large-area exposure of joints and reduce the risk of instability. In addition, the overall stability of the rock body can be improved by means of pre-stress anchoring, systematic support, and grouting to seal the fissures to ensure the safety and economy of mining [1; 2; 3]. Obtaining joint information involves three steps: joint recognition, geometric feature extraction, and statistical analysis. These steps are important in geology, rock mechanics, and engineering. Traditional ways to identify and analyze joints depend on on-site measurements. Common methods include compass measurements, photogrammetry, and rule-based image processing [4; 5]. These methods work in simple conditions but have clear drawbacks. They depend on manual work, which is slow. The results are affected by human errors and site conditions. Because of these limits, they cannot meet the needs of modern rock engineering, which requires precise, automated, and real-time solutions [6; 7]. In recent years, deep learning has improved quickly. This progress has led to big achievements in image segmentation. These improvements have helped fields like medical imaging, remote sensing, and target detection [8; 9; 10].