## 1 Introduction
Further, the dynamically changing operating environment during the process of advancing the face makes it difficult to adapt to the original lighting layout, and the coverage of light sources in some areas is insufficient. At the same time, when the coal mining machine's drum performs high-speed cutting on the coal wall, it produces a large amount of cutting dust that diffuses throughout the roadway. Although high-pressure spray dust-reduction technology [6] is used to eliminate dust, the droplets cannot adequately cover the dust particles, leading to a low combination efficiency between the water mist and dust. Coupled with limited ventilation in the mine, the unsettled water mist accumulates in the confined space, forming a high concentration of water mist in the environment. Therefore, in the complex environment where miners' features are easily obscured and blurred, traditional target detection algorithms often produce more missed detections and false detections [7], which seriously undermines the accuracy of intelligent identification and monitoring tasks in underground coal mines. The actual working condition of a coal mining face in an underground coal mine is shown in Figure 1.  
A schematic diagram of the hazardous area for personnel on the fully mechanized mining face is shown in Figure 2. In general, the working area of personnel on the fully mechanized mining face is usually on the outside of the scraper conveyor, and crossing the scraper conveyor to enter the inside is considered illegal operation. Therefore, to address the problem of miners invading dangerous areas for illegal operation, this paper adopts a deep learning-based object detection algorithm for miners' feature extraction and uses a binocular stereo matching algorithm to calculate the three-dimensional coordinates of the center position of the bounding box where the miners are located. This approach enables personnel localization and lays the foundation for subsequently determining whether the personnel have entered the illegal area or not. Environmental conditions such as heavy dust, water mist, and uneven illumination of the working face during the cutting process of the coal mining machine in the fully mechanized mining face seriously affect image quality and reduce target features, leading to deterioration in the effectiveness of object detection. To solve the problems of blurred target features and difficult feature extraction caused by water mist, dust, and uneven illumination in image data, this paper constructs a personnel detection network architecture, MSS-YOLO, for the complex environment of coal mining face. This architecture is based on the YOLOv8  
Figure 1: Actual working conditions of the fully mechanized mining face in an underground coal mine. Figure 2: Diagram of hazardous areas in fully mechanized mining face. The inner area of the enclosing box formed by the red straight line is the danger zone. object detection network, incorporating the characteristics of the environment of the coal mine. It aims to provide stable and real-time personnel detection information for personnel localization at the face. At the same time, combined with the SGBM binocular stereo vision matching method [8], the proposed approach achieves three-dimensional localization of miners in the working face. The main contributions of this paper are as follows:  
* To address the omission and misdetection issues of traditional deep learning algorithms in miner localization for an integrated mining face, this paper proposes the MSS-YOLO model. It achieves real-time and stable miner detection under uneven illumination, feature blurring, and varying dust concentrations. * The proposed MSS-YOLO model integrates a Multi-Scale Edge Enhancement (MSEE) module into the C2f module of YOLOv8 to strengthen edge representation and reduce missed or false detections caused by high dust conditions and jitter.