## 1 Introduction  
China holds abundant coal resources, with thick coal seams constituting approximately 45% of total reserves and 50% of national production [1, 2]. The top coal caving method remains predominant in thick-seam mining, where the development of intelligent top coal caving technology is crucial for achieving high-efficiency extraction [3; 4]. Accurate coal and gangue identification at the drawing opening as the fundamental prerequisite for intelligent longwall top coal caving faces. However, the underground environment presents persistent challenges, particularly high dust concentrations that significantly degrade detection accuracy. Consequently, developing reliable identification methods under dust interference has emerged as a critical research frontier [5; 6; 7]. The rapid advancement of deep learning and artificial intelligence technologies has significantly promoted the application of intelligent methods in coal gangue recognition at top coal caving working faces. Among these, image recognition technology has gained prominence due to its non-contact nature, high efficiency, and operational versatility [8; 9; 10]. Deep learning-based approaches eliminate the need for manual feature selection while demonstrating superior feature representation capabilities, rapid detection speeds, and high accuracy rates, making them a focal research area. In the research on image recognition and segmentation methods for coal and gangue, [PERSON] et al. [11] proposed a method for coal and gangue image segmentation based on the edge detection theory of the star algorithm, achieving a pixel area and centroid coordinate error rate within 0.29%. [PERSON] et al. [12] introduced a coal and gangue recognition method using Retinex and extreme learning machines, establishing a data acquisition platform and verifying its feasibility. [PERSON] et al. [13] developed a spectral data dimensionality reduction model using a two-dimensional autoencoder (2D-AE) and random forest, improving recognition accuracy with multi-spectral technology. [PERSON] et al. [14] trained a model based on infrared images and designed a convolutional neural network (CNN) for automatic recognition. [PERSON] et al. [15] proposed a lightweight ECSMv3_YOLOv4 network to enhance detection performance and speed under various conditions. In the research on model integration and optimization for coal and gangue recognition, [PERSON] et al. [16] developed a deformable convolution YOLOv3 model and data acquisition platform, verifying its accuracy under different sizes and lighting conditions. [PERSON] et al. [17] designed a CGR-CNN model using thermal images to validate coal and gangue recognition. [PERSON] et al. [18] integrated multiple preprocessing techniques and near-infrared spectra with a neural network (CBAM and ViT), improving identification accuracy. [PERSON] et al. [19] combined CNN and SVM for faster recognition and improved gangue identification. [PERSON] et al. [20] introduced an improved YOLOv5.1 model, achieving 98.34% accuracy and a detection time of 3.62 s. [PERSON] et al. [21] proposed a feature pyramid and empty space pyramid pooling-based UNet model for improved segmentation. [PERSON] et al. [22] designed a multi-layer LSTM model with multi-source feature fusion of image and vibration for better recognition. [PERSON] et al. [23] developed an XBS-YOLOv5s-based gangue identification method for underground production environments. [PERSON] et al. [24] improved the VGG16 model using migration learning, though the recognition accuracy remained low. [PERSON] et al. [25] combined the BSP and SSD models to propose a fast and accurate coal gangue detection method. In the implementation of deep learning technologies, a variety of software solutions are available to aid in the development, training, and deployment of models. Commonly used software frameworks include TensorFlow, PyTorch, Caffe, MXNet, Microsoft Cognitive Toolkit (CNTK), and ONNX (Open Neural Network Exchange), among others [26; 27; 28].