# Coal-Rock Data Recognition Method Based on Spectral Dimension Transform and CBAM-VIT
## 1 Introduction  
Coal mine intelligence plays a crucial role in achieving the high-quality development of the coal industry [1]. The identification and sorting of coal-gangue is a key technology for intelligent coal mine construction and improving the accuracy and efficiency of coal seam detection, intelligent mining, and rapid sorting [2]. The traditional methods for coal-gangue separation mainly include dense medium separation, flotation, gravity separation, magnetic separation, and electrostatic separation. These methods present certain challenges in terms of separation efficiency, processing capacity, cost, and environmental friendliness. In recent years, coal and gangue separation technologies have been continuously developed. Currently, the main methods for coal-gangue identification include X-ray, \(\gamma\)-ray, image monitoring, radar detection, and vibration detection [3]. The X-ray and gamma-ray detection methods rely on distinguishing the principal constituent elements of coal and gangue; however, there are radiation risks associated with radiographic detection methods, posing potential harm to human health [4; 5; 6]. With advancements in computer vision technology, the potential of using image recognition technology for coal-gangue separation has been explored [7; 8]. [PERSON] et al. [9] employed the gray-level co-occurrence matrixto extract texture features from coal-rock images and established a coal-rock classification method, using Fisher discriminant analysis. [PERSON] et al. [10] utilized energy, contrast, correlation, and other feature vectors based on the differences in texture characteristics of coal-rock for coal-rock identification. However, surface and texture differences may not be visible in images due to various interfering factors, like uneven lighting and poor illumination. Additionally, the presence of dust around the gangue can also impact recognition performance. Therefore, it is highly significant to develop a recognition method with high efficiency, low operational costs, and no harm to human health. The near-infrared spectrometer is an advanced instrument that utilizes information measurement and processing technology. It offers several advantages, including high analysis efficiency, stable results, non-destructive measurement, ease of operation, and wide applicability [11]. The identification mechanism relies on the specific chemical composition and material structure of the substance being analyzed, which exhibit absorption characteristics in the reflection spectrum at specific wavelengths. In recent years, near-infrared spectrometry has experienced rapid development in various fields, such as food, agriculture, and coal [12; 13; 14]. [PERSON] et al. [15] proposed an improved multilayer extreme learning machine algorithm combined with visible-infrared spectroscopy, which established a rapid coal identification model. The accuracy rate of the test set reached 92.25%. [PERSON] et al. [16] conducted research on coal and rock classification based on joint analysis of visible light-near infrared and thermal infrared. [PERSON] et al. [17] utilized visible and near-infrared (VIS-NIR) reflectance spectroscopy for coal-rock identification. In the domain of image recognition, one study [18] explored a method for coal-gangue color image recognition based on multispectral and color texture fusion. Another study [8; 19] developed a coal-gangue recognition model, using support vector machines, random forests, and feedforward neural networks, extracting multidimensional features from gray texture in coal-gangue images. Additionally, the literature [20] employed the improved LeNet-5 deep neural network to train coal-gangue samples and obtained a deep learning model for coal-gangue classification. However, direct acquisition of coal-gangue images is susceptible to environmental disturbances, and the classification accuracy of deep learning models can be relatively low.