## 5 Practical Significance and Realistic Challenges  
The proposed coal-rock recognition model holds substantial practical value in real-world mining applications. Its ability to accurately identify and segment coal and rock \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experimental Group** & **1** & **2** & **3** & **4** & **5** \\ \hline U-Net & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Full Scale Connections & & & & \(\surd\) & \(\surd\) \\ Res2 Block & & \(\surd\) & \(\surd\) & & \(\surd\) \\ Residual Attention Module & & & \(\surd\) & & \(\surd\) \\ mIOU & 95.78 & 96.42 & 97.01 & 96.55 & **97.99** \\ mPA & 97.75 & 97.95 & 98.47 & 98.20 & **99.28** \\ Quantity of participants/\(10^{6}\) & 24.89 & 25.83 & 23.87 & 25.36 & 24.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of experimental results of ablation of image segmentation modules. Figure 14: Comparison of image segmentation module ablation experiment results. Subfigures (**a–d**) present different representative samples selected from the dataset to demonstrate the module’s effectiveness across various coal–rock scenarios. regions contributes to improved operational efficiency and enhanced safety. By enabling precise recognition, the model reduces the need for manual intervention, streamlines mining processes, and minimizes the risk of human error. Moreover, integrating the model into intelligent monitoring systems facilitates the real-time analysis of mining activities, thereby supporting data-driven decision-making and advancing the broader objective of intelligent mining [27]. The model's interpretability further enhances its applicability by providing insight into prediction outcomes, which is particularly valuable in complex scenarios where understanding the rationale behind model decisions is critical. Nevertheless, deploying the model in real underground mining environments presents several challenges. One of the primary issues is the variability in the environmental conditions in mines. Dynamic lighting environments--ranging from low illumination to high reflection or shadowed areas--can adversely affect input image quality and, consequently, model performance. Additionally, dense dust and particulate matter may obscure key visual features, complicating accurate segmentation. Variations in humidity and temperature can also impact both imaging equipment performance and data quality. Another critical challenge involves hardware compatibility. Many existing mining systems lack the computational resources necessary to efficiently run deep learning models. This necessitates the development of lightweight model variants or the adoption of edge computing solutions to alleviate dependence on centralized infrastructure. Moreover, the heterogeneity of imaging devices across different mining sites may require additional preprocessing to standardize inputs and maintain consistent performance. Several strategies can be employed to address these challenges. Enhancing the model's adaptability to environmental variation is crucial. This may involve further optimizing the multi-scale brightness-enhancement module to accommodate a broader range of lighting conditions and integrating advanced denoising techniques to mitigate dust interference. To improve deployment feasibility, model compression techniques--such as pruning and quantization--can be applied to reduce computational demands. In parallel, edge computing frameworks may be leveraged to enable real-time, localized inference. Diversifying the training dataset is also essential to improving model generalization. Incorporating data from multiple mining sites with varied geological and environmental characteristics can enhance robustness. Additionally, transfer learning approaches may allow for rapid adaptation to new environments with minimal retraining overhead.