# Improved FraSegNet-Based Rock Nodule Identification Method and Application
## 1 Introduction
Deep learning has also shown potential for finding nodal fissures in rocks. Feature-based algorithms use pixel data to identify and extract nodules [11]. However, these methods are specific to certain situations and lack flexibility [12; 13; 14; 15]. Compared to traditional methods, deep learning is faster and more flexible. It is also stronger and works well with challenges like complex rock shapes, poor lighting, and noise [16; 17; 18]. The U-Net network is a well-known image segmentation model. It is good at handling small datasets and complex edges [19; 20]. [PERSON] et al. [21] proposed an image classification model for detecting cracks using atrous convolution, ASPP module, and depth-separable convolution. [PERSON] et al. [22] used a fast-RCNN network to automatically detect the acoustic image logging of boreholes of such structures with an accuracy of up to 90%. [PERSON] et al. [23] proposed an intelligent image segmentation model based on ResNet and Unet, called RUnet, in which ResNet was used as a pre-training model to extract image features. The optimizer and loss function of RUnet were improved by incorporating the domain knowledge of geology. [PERSON] et al. [24] successfully identified the background, cracks, and laminations in the image log data using Mask-RCNN. However, when segmenting real rock nodal fissures, these methods still have limits. They have trouble recognizingsmall fissures and often create blurry edges. To fix this, combining geological features with deep learning is important. This approach improves network design and makes joint and fissure extraction more accurate. FraSegNet is an advanced segmentation algorithm that boosts image accuracy. It uses a modern neural network and a new attention mechanism. FraSegNet provides a solution for automatically recognizing rock joints. Built on the encoder-decoder model, it improves feature extraction and increases accuracy. It achieves this through multi-scale feature fusion and adaptive weighting. This allows for the precise segmentation of nodal areas and better recognition of nodules at different scales [25]. To establish a connection between nodal fracture characteristics and rock mechanical properties, this study employs the Barton-Bandis criterion based on the extracted nodal fracture parameters such as density, width, and inclination. By analyzing how these parameters influence key mechanical properties like cohesion, friction angle, and elastic modulus [26], we develop mapping equations that link fracture features to rock mass behavior. The Hoek-Brown criterion is then applied to validate the accuracy of the optimized FraSegNet network by comparing parameter extraction errors. The proposed method provides a robust framework for joint identification and mechanical parameter mapping, offering valuable insights for both theoretical research and practical engineering applications. By improving the reliability of rock engineering design and stability analysis, this study contributes significantly to advancing our understanding of rock mass behavior and enhancing the accuracy of geological modeling.