## 1 Introduction  
The development degree of joints on the tunnel face reflects rock mass integrity, which is crucial for dynamic evaluation during tunnel construction. Currently, joint development descriptions rely on hand-drawn records and qualitative judgments, limiting accuracy and efficiency. Digital image capturing is primarily used for records, yet these images contain valuable rock mass information. If an effective method can be established to digitally extract and obtain complete rock mass information from tunnel face images, it would significantly improve the efficiency of real-time dynamic grading of the surrounding rock on construction sites.  
Early methods, such as manual counting, were inefficient and prone to human error. [PERSON] and [PERSON] [1] first used camera images for rock mass characterization, while subsequent studies introduced digital image processing techniques [2] for better accuracy and efficiency. Advancements in image processing, such as Fourier and Hough transforms [3, 4], grayscale elevation methods [5], and structural analysis techniques [6], have improved joint extraction but still rely heavily on manual intervention and experience. Recent studies [7, 8] developed algorithms to overcome these limitations but faced challenges in complex environments. However, traditional image processing methods heavily rely on experience, and their processing effectiveness still needs improvement.  
AI algorithms, especially deep-learning-based object detection methods, have shown promise in civil engineering for recognizing cracks in concrete linings [9; 10; 11; 12; 13; 14; 15; 16]. However, applying these methods to tunnel rock faces poses challenges due to larger areas, harsh conditions, and complex backgrounds. Many researchers have conducted studies on this topic. For instance, [PERSON] et al. [17], [PERSON] et al. [18], and [PERSON] et al. [19] introduced modifications to existing networks to enhance performance in pixel-level joint extraction. Recent advancements, such as the Path Aggregation Network (PA-Net) [20], have further improved detection accuracy in dark environments.  
Using deep learning neural network models for joint extraction is currently the mainstream research direction for tunnel face joint extraction methods. However, the extraction accuracy still needs improvement. Upgrading and adapting the structures of deep learning convolutional neural networks and deepening the learning for specific engineering geological rock masses are the main breakthroughs at present. Among the many image segmentation convolutional neural networks, the U-net network, proposed in 2015 for biomedical image segmentation [21], has been applied by some researchers in the process of crack or joint extraction due to its simple and intuitive architecture, relatively low training data requirements, and ability to generate pixel-level masks [22; 23; 24; 25]. Despite progress, extraction accuracy needs further improvement. This study focuses on enhancing deep learning convolutional neural networks, particularly the Mask R-CNN model [26], for better joint extraction accuracy. The Mask R-CNN, proposed in 2017, offers high precision in object detection and segmentation and is well-suited for complex tasks. It has been widely adopted in various fields [27; 28; 29; 30; 31; 32]. It can be considered for introduction into the process of extracting complex tunnel face joints.  
This paper utilizes data from the Henan Luanlu Expressway Nianpan Tunnel and Zhejiang Hangwen Railway Tunnel to compare joint extraction results using traditional methods, the U-net model, and the Mask R-CNN model. We aim to demonstrate the superiority of the Mask R-CNN in achieving accurate and efficient joint extraction.