## 1 Introduction
In addition, the single-stage approach represented by a series of YOLO algorithms, which did not need to generate the area suggestion network in advance, could directly obtain the category and coordinate information of the target object [13], and had a faster detection speed and powerful target detection capability, thus the algorithm has been favored by many researchers. [PERSON] et al. [14,15] proposed an approach of combining segmentation networks with YOLOv5, which enabled the network to take into account both target segmentation and target detection and achieved integrated multi-task detection. They used this method and studied the belt conveyor runout faults, and enhanced the network's capability to detect straight lines by combining the outputs of the improved YOLOv5 network, thus solving the problem of fast feature extraction and deviation judgment of conveyor belt edges in complex backgrounds. [PERSON] et al. [16] improved the YOLOv5 model and put forward an approach to classify coal gangue on the basis of multispectral imaging techniques and object detection. Not only could it achieve the precise identification of the gangue, but it also obtained information about the relevant locations of the gangue, which could be applied to the target detection task of the gangue. [PERSON] et al. [17] studied the classification and location of coal and gangue, proposed a dynamic object detection approach in view of CG-YOLO, and used robots to grab irregularly shaped gangue to form a complete separation system. [PERSON] et al. [18] improved the backbone and neck of YOLOv5, implemented a deep lightweight target inspection network, and ensured speed and precision in detecting multiple types of materials on belt conveyors. [PERSON] et al. [19] studied the issues of remote sensing object detection and proposed a new remote sensingobject detection model using YOLO-v3 to raise the precision and real-time performance of remote sensing object detection. [PERSON] et al. [20] added a lightweight non-parametric attention mechanism based on YOLOv7 and introduced deep separable convolution in the backbone network to achieve efficient recognition of foreign objects. It is worth mentioning that the precision of the image-based recognition method is easily affected by the downhole lighting conditions, material surface, working environment, and camera exposure time, in addition to the camera's mounting angle, height, and focal length settings which will also have an impact on the recognition efficiency. Image recognition technology based on transmission imaging relies on high-energy rays (e.g., gamma rays, X-rays) to conduct transmission imaging of the raw coal flow on the conveyor belt and to differentiate between coal and gangue mixture on the basis of the fact that coal and gangue have different densities. [PERSON] et al. [21] developed a coal gangue-identifying system according to natural gamma ray technology, which achieved the automation of coal and gangue identification. [PERSON] et al. [22] combined dual-energy X-ray with Geant4 simulation image processing technology, and used the R-value method to effectively identify coal and gangue of various shapes and thicknesses. [PERSON] et al. [23] proposed a linear function fitting method as a basis for obtaining atomic numbers of uncertain materials, which provided the possibility of separating the atomic numbers of substances with unit resolution. [PERSON] et al. [24] combined the receptive field module with the U-Net model to provide a reference to address the issue of gangue clinging or obscuring impacting the precision of intelligent recognition. The above scholars have made great progress in the research of automatic gangue sorting systems and gangue identification methods, but there are still shortcomings. Most of the research on automatic gangue sorting systems is in the experimental stage, which has limited ability to sort gangue with large particle size and dense distribution, and cannot overcome the influence of the poor underground environment.