## 6 Summary and Prospect
### Future Research Directions  
Although the proposed model has shown strong performance in coal-rock recognition tasks, several important limitations and deployment challenges remain. Addressing these issues will be the focus of our future work to enhance the model's reliability and industrial applicability.  
First, the model's architecture integrates multi-scale luminance enhancement, residual attention, and Transformer-based modules, which collectively increase its depth and parameter count. While this design improves recognition accuracy under complex visual conditions, it also raises the risk of overfitting, particularly when applied to limited-scale datasets. To mitigate this, we plan to explore model simplification techniques such as pruning, quantization, and knowledge distillation, while investigating uncertainty-aware learning to improve robustness on ambiguous inputs.  
Second, although preliminary measurements suggest that the model can process \(384\times 384\) images within 82 ms on mid-range GPUs, practical mining deployments often operate under strict hardware constraints. Therefore, we are actively developing lightweight variants of the model tailored for edge deployment using embedded GPU platforms such as NVIDIA Jetson Xavier NX. This will allow real-time inference on mobile or autonomous inspection systems in underground environments.  
Third, the current model is trained on coal-rock datasets with relatively stable geological textures. In real-world operations, the model may encounter unseen rock types, variable mineral compositions, or abrupt structural anomalies. To address this, we will incorporate domain generalization and transfer learning methods to enhance adaptability across different mines and regions.  
Fourth, to systematically evaluate model performance under controlled variations, we are in the process of constructing an underground coal-rock simulation environment. This setup is designed to replicate multiple mining conditions, including different coal-rock material compositions, lighting intensities, dust concentrations, and surface reflectance. It will support the development of a more realistic dataset and provide a valuable platform for testing generalization, robustness, and sensor integration under extreme conditions.  
Finally, the current framework primarily relies on visual information, which may degrade under conditions such as occlusion, heavy dust, or poor lighting. Therefore, future research will also explore multi-modal learning strategies by integrating complementary sensing data (e.g., acoustic, thermal, or LiDAR) to achieve more robust and comprehensive underground scene understanding.  
Methodology, B.J.; conceptualization, C.S.; data curation, S.Q.; software, W.W.; writing--original draft, Y.W.; validation, Y.L.; writing--review and editing, Z.W.; resources, D.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Fund for the National Key Research and Development Program of China [2022 YFB3205804], by the Fund for the National Key Research and Development Program of China [2022 YFC2905700], and by the Fund for the Fundamental Research Programs of Shanxi Province [202203021221106]. This paper is supported by the opening project of State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China. The opening project number is 2011 DA105287-FW202408.  
Not applicable.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ongoing related research and confidentiality agreements with industrial partners involved in the data collection process.  
The authors would like to thank [PERSON], School of Electrical and Control Engineering, North University of China, and [PERSON], Chongqing University, who provided resources and financial support for this research.  
The authors declare no conflicts of interest.