Downhole Coal-Rock Recognition Based on Joint Migration and Enhanced Multidimensional Full-Scale Visual Features  
[PERSON]  
1 School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China; [EMAIL_ADDRESS] (B.J. ); [EMAIL_ADDRESS] (W.W.); [EMAIL_ADDRESS] (Y.W. ); [EMAIL_ADDRESS] (Z.W. ); [EMAIL_ADDRESS] (D.S.) [PERSON]  
1 School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China; [EMAIL_ADDRESS] (B.J. ); [EMAIL_ADDRESS] (W.W.); [EMAIL_ADDRESS] (Y.W. ); [EMAIL_ADDRESS] (Z.W. ); [EMAIL_ADDRESS] (D.S.) [PERSON]  
3 School of Mechanical and Electrical Engineering, Beijing Institute of Technology, Beijing 100081, China 3* 3*  
YOLOv2--a target localization approach based on regression-driven object detection--with a linear imaging model for identifying coal-rock formations in underground environments. [PERSON] et al. [8, 9] proposed coal-rock recognition methods for fully mechanized mining faces, utilizing an improved U-Net architecture and a deep convolutional neural network, respectively. [PERSON] [10] addressed the data scarcity problem by introducing VarConSinGAN for sample generation and feature migration. [PERSON] et al. [11] introduced a tower pooling-based coal-rock recognition framework, while [PERSON] et al. [12] proposed an improved DeeplabV3+ model with transfer learning for interface segmentation. [PERSON] et al. [13, 14] developed methods integrating an enhanced YOLOv3 with cubic spline interpolation and proposed a segmentation framework incorporating attention mechanisms and full-scale connectivity. To capture detailed structural variations, the complete local binary pattern (CLBP) was adopted for modeling coal-rock image features [15]. [PERSON] [16] and [PERSON] [17] designed humanoid intelligent control strategies for mining height regulation, combining fuzzy inference and particle swarm optimization with improved YOLOv3-based boundary detection. [PERSON] et al. [2] applied a U-Net-based model incorporating the ResNeSt module and channel attention for semantic segmentation. [PERSON] et al. [18] proposed a multi-scale feature fusion method based on local binary patterns and convolutional neural networks. [PERSON] et al. [19, 20] utilized DeepLabV3+ with a sparrow search algorithm for mineral processing image feature extraction, and further improved YOLOv5 to enhance the detection accuracy of mineral separation points under complex operational conditions. However, most of these programs use more traditional image-enhancement methods to preprocess coal-rock images, so the enhancement effect is limited and insufficient for the recognition network. Most coal and rock image research is in the target detection field, the classification of coal and rock images is still classified by blocks, and the data of coal and rock areas are still not precise, thereby providing incorrect information to the coal mining machine so it cannot realize the precise cut-off. To address this issue, we propose a recognition model tailored for underground coal-rock scenes that integrates joint feature migration and enhanced representation across multidimensional and full-scale visual spaces. A public dataset was employed to train this network and enhance the generalizability of the model to cope with the harsh underground coal mine environment. To overcome the uneven brightness in the image caused by unnatural parallel light irradiation in the coal mine, the multi-scale module enhances the brightness of features under different sensory field ranges to prevent possible local overexposure or local underexposure. The NR-Transformer module performs multidimensional noise suppression by capturing long-range dependencies within the image, enabling the network to achieve pixel-wise segmentation on low-quality coal-rock data collected from underground mining environments. The encoder is designed to extract deep semantic representations through a combination of convolutional pooling layers, the Res2 module, and a multi-channel residual attention mechanism.