# Coal-Rock Data Recognition Method Based on Spectral Dimension Transform and CBAM-VIT
## 5 Conclusions  
To address the problem of inaccurate recognition of coal-gangue in the complex and harsh underground mining environment, a data fusion method, followed by an identification method, is proposed. The core techniques include converting the collected spectral data into RGB images by using three preprocessing methods and employing the fusion of CBAM and VIT models for classification and identification. In order to verify the practical application effectiveness of the proposed algorithm, a coal-gangue identification verification platform was constructed in the laboratory and tested to simulate the complex environment in underground mines. The research findings are as follows.  
1. A coal-gangue image classification network model based on a hybrid attention mechanism (CBAM-VIT) is proposed, which achieves deep-level extraction of semantic features of coal-gangue through channel attention structure, spatial attention structure, and convolutional structure. The network model is trained using the cross-entropy loss function to automatically learn deep-level semantic feature representation and classify coal-gangue images.
2. CBAM-VIT enhances the feature expression capability of coal-gangue in complex environments. By fusing spatial and channel features, it more effectively integrates the multilayer feature information of coal-gangue RGB images. The transformer layer captures the long-range dependencies between image elements, capturing useful information and suppressing the irrelevant noise of different granularities in the feature maps. Compared to the VIT algorithm, the proposed CBAM-VIT algorithm achieves an average recognition accuracy improvement of 3.2%, enhancing the precision and reliability of classification. This demonstrates that the method has stronger robustness compared to other approaches.
3. This study set up a simulated coal mining environment with three levels and four influencing factors, considering only four environmental factors. There are still other influencing factors in the underground mining environment, and there exist certain discrepancies between the simulated environment and the actual working face in coal mines. Further research is needed to continuously improve the methodology.  
Conceptualization, J.Y. and Y.Z.; methodology, Y.Z.; software, K.W.; validation, Y.Z., K.W., and Y.T.; formal analysis, J.L.; investigation, J.Y.; resources, Y.Z.; data curation, Y.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, J.Y.; visualization, Y.Z.; supervision, G.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Theory and Method of Excavation-Support-Anchor Parallel Control for Intelligent Excavation Complex System (Grant No. 52104169), and National key research and development program (Grant No. 2022 YFB4703703). Green, intelligent, and safe mining of coal resources (Grant No. 52121003).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The study was approved by the China University of Mining and Technology (Beijing).  
The authors declare no conflict of interest.