## 1 Introduction  
As a large coal-producing country, China's annual coal production is up to billions of tonnes, but the gangue content of the mined raw coal is as high as 10% to 30%. The low carbon content and high ash characteristics of gangue cause a decline in the quality of raw coal, thus affecting the use value and economic value of coal [1]. In addition, in the main coal flow transport system, the presence of large gangue may lead to longitudinal tears, surface scratches, and other damage to the conveyor belt of the belt conveyor system, and if these problems are not detected and dealt with promptly, the damage may further expand or even lead to the occurrence of major accidents [2]. Thus, the efficient sorting of gangue is the first important link in the deep processing of coal. Manual gangue sorting is gradually being eliminated due to high labor intensity, poor working environment, and low efficiency [3]. To change the above gangue sorting method and enhance the level of coal mine intelligence, many scholars have conductedin-depth research on an automatic gauge sorting system. [PERSON] et al. [4] used machine vision as a means of identifying gangue and then utilized a high-pressure air gun to reject it. An intelligent sorting system for gangue based on an embedded artificial intelligence development platform was proposed by [PERSON] et al. [5]. Image acquisition and processing were completed by the intelligent vision systems. After aligning the camera coordinate system with the robot coordinate system, the robot arm was controlled to pursue and classify the gangue. A near-infrared camera was used with a visible light camera to build a multi-level fusion recognition system by [PERSON] et al. [6] to provide a new idea for the robot to automatically sort coal and gangue. [PERSON] et al. [7] established a smart visual coal gangue separation framework on the basis of CoppeliaSim, modeled the raw coal and gangue blends, manipulator control system, material conveyor belt, and image recognition system, and restored the actual working conditions of gangue splitting. [PERSON] et al. [8] integrated machine vision with robotics to construct a gangue robotic sorting system, and optimized the robot controller to improve control precision. In the coal gangue automatic sorting system, as only the precise identification of coal and gangue can control the subsequent actuators to sort coal and gangue, the precise identification of coal and gangue is particularly important. With the advancement of computing power and the development of intelligent algorithms, research on coal gangue recognition technology shows a clearly deepening trend. Currently, the mainstream recognition methods include visible light-imaging-based image recognition technology and transmission-imaging-based image recognition technology. Image recognition technology based on visible light imaging uses a camera to image the flow of coal on the conveyor belt and adopts machine learning algorithms to classify coal and gangue based on the different textures, grey values, and other feature information presented on the digital image. [PERSON] et al. [9] proposed the idea of combining infrared images with convolutional neural networks (CNN) to distinguish coal and gangue. [PERSON] et al. [10] proposed an image-based deep learning framework for coal and gangue stratification detection, which provided a solution to the problem of multi-scale extraction of image features and the difficulty of recognizing multiple randomly located targets. [PERSON] et al. [11] raised a way of accessing an improved LeNet5 model to identify coal gangue, which could automatically extract coal gangue image features and precisely classify them. Based on the idea of transfer learning and VGG16, [PERSON] et al. [12] constructed a CNN model to distinguish between coal and gangue images, which had a good recognition effect.