## 1 Introduction  
Coal resources around the world are an indispensable energy source, representing an economic, clean, and efficient use of fossil energy. It has been determined that in the future, coal resources will still be one of the main energy sources in the world's energy structure and will be difficult to replace. As the most important working environment in underground coal mines, fully mechanized mining faces usually utilize commonly used coal mining machinery to complete the coal mining process, including scraping, room and pillar cutting, etc. The normal operation of mechanized working faces inevitably requires personnel and equipment to work together, and in the limited space, this can easily lead to personnel exposing themselves to operational risks. Therefore, it is necessary to ensure the safety of personnel in fully mechanized mining faces by means of intelligent detection and monitoring systems, as well as timely warnings of unsafe behaviors and potential accidents in the complex and changeable working face environment. At present, the methods for detecting and determining the illegal operation and abnormal behavior of personnel in fully mechanized mining faces mainly include the traditional manual monitoring method [1], ultra-wideband wireless communication (UWB) [2], and computer vision detection technology [3]. Among them, the traditional manual inspection and monitoring methods are easily affected by the subjective judgment of personnel, and at the same time, prolonged monitoring work can easily lead to fatigue among the monitoring personnel, reducing their attention and alertness, meaning they may ignore important abnormal situations and fail to send out timely alarm information, thus creating a hidden safety danger. Based on ultra-wideband wireless communication (UWB) technology, which relies on the measurement principle of TOF and TDOA, the time difference of the signals between two ultra-wideband modules is used to calculate the relative distance between the personnel and the laser, allowing to measure the personnel's position. However, due to the influence of the dust concentration in fully mechanized mining faces, as well as occlusion, the positioning error generated by UWB cannot meet the demand for precise positioning of personnel underground in coal mines. The positioning information obtained by computer vision-based personnel positioning methods is more intuitive and stable. These methods usually use object detection or semantic segmentation algorithms under stereoscopic vision for miners' feature extraction and then combine the internal and external references of the camera to obtain the personnel's position information under the working surface. This technology has several advantages: it is non-contact and low-cost, has high accuracy and stability, avoids cumulative errors, etc. Therefore, at present, most fully mechanized mining faces adopt computer vision localization methods for personnel localization. In previous work, we carried out extensive research on personnel detection in fully mechanized mining faces; developed the DsLMF+ dataset for the detection of coal mine personnel, their postures, and other related objects [4]; and utilized the data to detect miners and their postures in two-dimensional images. The method mainly utilizes the YOLOv7 object detection algorithm to detect and estimate the attitude of the personnel in an image of an integrated mining face captured by an industrial camera. In the course of the study, it was found that the narrow space of the roadway and the dense stacking of equipment limit the working range of miners, and the personnel features are often obscured by equipment such as hydraulic supports and coal mining machines [5]. In addition, the complex spatial structure of the underground environment leads to limited installation of lighting equipment, and the layout of large mechanical equipment in the fully mechanized mining face will block the light sources and form a shadow area.