## 2 Related Work
[26] proposed a novel mask-guided multi-level fusion network (M2 FNet) for RGB-T pedestrian detection in order to solve the problem of modal information contamination that is characteristic of RGB-T pedestrian detection methods. They tested it on the KAIST and CVC-14 RGB-T pedestrian detection datasets, which achieved more excellent results. [PERSON] et al. [27] proposed the MEAG-YOLO model for the timely and accurate detection of the use of workers' personal protective equipment (PPE) in complex scenarios in substations, which effectively solves the problem of inaccurate detection results caused by traditional algorithms due to different target sizes, complex backgrounds, and multiple model parameters. [PERSON] et al. [28] used YOLOX for personnel detection in order to improve the efficiency and level of laboratory safety management and to monitor abnormal behaviors such as smoking and incorrect wearing of personal protective equipment (PPE) such as laboratory coats, hats, masks, and gloves. At the same time, the steps of human key point detection, posture estimation, and behavioral recognition were used to realize the abnormal behavioral discrimination of laboratory personnel. Meanwhile, for other target detection tasks in complex scenarios, scholars have proposed targeted improvements in their respective fields. [PERSON] et al. [29] proposed a multi-target detection algorithm based on the combination of YOLOv5 and Distance Intersection Over Union loss Non-Maximum Suppression (DIOU-NMS) for the omission problem of traditional algorithms when detecting dense small targets under aerial images, which effectively solves the detection performance of dense small targets under complex backgrounds. [PERSON] et al. [30] proposed a DCAM-YOLOv5 target detection algorithm for use on ground-penetrating radar (GPR), which solves the problems of low accuracy of traditional methods in detecting multiple types of defects in tunnel lining and the difficulty of feature extraction under fuzzy boundaries. In summary, in various applications in complex industrial environments, the simplicity of deployment, real-time performance, and good target detection of the YOLO series of algorithms have led researchers to make targeted improvements to the algorithms. This has led to mature application of these variants of the structure in most industrial fields, especially in the study of pedestrian detection methods, and the YOLO series has been highly favored by researchers. However, due to the complex environmental characteristics such as high dust, high noise, weak texture, and uneven illumination in the fully mechanized mining faces of underground coal mines, the existing models are insufficient to fully solve the task of personnel detection in a fully mechanized mining face. Therefore, building on our previous research and adopting the YOLOv8 object detection algorithm [31], we built the MSS-YOLO model by designing an MSEE, SCDH, and SPSC modules to carry out the recognition and detection of miners in a fully mechanized mining face. Meanwhile, based on the miner detection results, SGBM binocular stereo vision matching technology is integrated to compute the miners' 3D spatial coordinates. The specific details of the network architecture and the miner localization method will be mentioned in the following section.