## 5 Analysis of Coal Gangue Image Processing and Recognition Results  
### Comparison of Different Segmentation Models  
Under identical training parameters and datasets, the modified DeepLabV3+ model proposed in this study was trained for coal and gangue recognition. It was then compared with several widely used models, including PSPNet, PSANet, DeepLabV3+, and UNet. The variation curves of the loss function and the evaluation metrics are presented below.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **mAcc/\%** & **mIoU/\%** & **mPA/\%** & **Param/m** \\ \hline A & 96.03 & 96.27 & 95.81 & 41.91 \\ B & 96.07 & 95.79 & 95.83 & 26.04 \\ C & 96.50 & 96.13 & 95.92 & 26.51 \\ D & 97.34 & 96.49 & 96.06 & 28.02 \\ E & 97.88 & 97.19 & 96.86 & 28.51 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of ablation experiment.  
Figure 10: Training loss value variation curve.  
As shown in Table 3, the proposed model achieves a mean accuracy (mAcc) of 97.88%, an improvement of 1.85% over the original DeepLabV3+. The mean Intersection over Union (mIoU) reaches 97.19%, representing a 0.92% increase, while the mean pixel accuracy (mPA) improves by 1.05% to 96.86%. The parameter count is significantly reduced from 41.91 M to 28.51 M, with floating-point operations (FLOPs) decreased by 31.97%. These results indicate that the proposed improvements not only enhance segmentation accuracy but also reduce model complexity. When compared with other models, the proposed model demonstrates superior performance in coal gauge image segmentation. It achieves the highest scores across key metrics, including mAcc, mIoU, and mPA, highlighting its outstanding segmentation capability and accuracy compared to PSPNet, PSANet, the original DeepLabV3+, and UNet.  
Figure 11 presents the performance of the five models on the validation dataset, coal is identified by red color and gangue by green color during the segmentation process. To facilitate a more detailed analysis, two specific regions (A and B) were selected for magnification and comparison. In region A, there are more overlapping parts of coal and gangue, which are prone to recognition errors; in region B, the colour of coal and background are closer, which are prone to recognition errors, so the choice of these two regions is more responsive to the accuracy of the model in facing complex working conditions. In region A, all models, with the exception of the proposed model in this study, failed to accurately segment the coal gangue. Both PSPNet and PSANet erroneously classified the coal gangue area as red coal blocks, indicating classification errors. Although DeepLabV3+ and UNet successfully identified the gangue, their boundary segmentation lacked sufficient precision. In contrast, the model proposed in this study achieved accurate segmentation of the coal gangue in region A, with clear and precise boundaries. In region B, both PSANet and UNet failed to segment the coal blocks, leading to omissions. DeepLabV3+ and PSPNet partially identified the coal blocks but were unable to achieve full segmentation. These results validate that the incorporation of the ECA module enables the model to effectively capture the edge features of both coal and gangue, thereby achieving precise segmentation in this region. Collectively, these findings demonstrate that the model proposed in this study outperforms existing methods in terms of accuracy and detail preservation, highlighting its capability in handling complex segmentation tasks.