## 2 Related Work  
Image data-based methods for accurate positioning of personnel in integrated mining faces of coal mines mainly rely on the stability and robustness of personnel detection. Currently, personnel detection algorithms can be categorized into traditional detection methods and deep learning-based personnel detection methods. Traditional image-based personnel detection methods mainly extract features such as the target's grayscale, texture [9], and color information to complete the detection task. Currently, common traditional people detection algorithms include Viola-Jones [10], HOG+SVM [11], DPM [12], and Soft-NMS [13], etc. Before the advent of deep learning-based people detection algorithms, traditional people detection algorithms could accomplish the people detection task in simple scenarios in a relatively stable manner. With the development of computer technology and industrial intelligence, the demand for intelligent monitoring of staff status and location under complex working conditions has gradually risen. However, traditional personnel detection methods in complex environments have low detection efficiency, poor real-time performance, and cannot ensure good stability. Therefore, researchers have begun to use deep learning-based object detection algorithms to establish personnel detection models, thus completing the task of personnel detection in industrial application scenarios. Currently, deep learning-based object detection algorithms are generally categorized into two types: region nomination-based and end-to-end methods. Among them, region nomination-based object detection algorithms achieve high-precision object detection by generating candidate regions and performing target classification and localization within these regions. The more mainstream region nomination-based object detection algorithms today are R-CNN [14], Fast R-CNN [15], and SPP-net [16]. End-to-end object detection algorithms generate target categories and bounding boxes directly from the input image without intermediate candidate region nomination steps, which is faster and more efficient. Currently, the more mainstream end-to-end object detection algorithms are YOLO [17], SSD [18], EfficientDet [19], and DETR [20]. In current industrial application environments, deep learning-based personnel detection has achieved remarkable success. [PERSON] et al. [21] proposed an improved Faster R-CNN network for the problem of difficult detection of pedestrians in low-quality images under complex backgrounds and realized the detection of road pedestrians at different time periods. [PERSON] et al. [22] proposed a road object and pedestrian detection method based on YOLOv8, which solves the problem of missed detection and high false detection rates of pedestrians under foggy conditions by adjusting hyperparameters such as epochs, batch size, and augmentation methods. [PERSON] et al. [23] proposed a detection model based on improved YOLOv7-Tiny in order to improve the detection accuracy of an object detection algorithm for vehicles and pedestrians in traffic scenes and to solve the problem of small target leakage detection, achieving real-time and highly stable detection on the embedded platform NVIDIA Jetson AGX Xavier. [PERSON] et al. [24] proposed a lightweight object detection model integrating GhostNet and YOLOv5s to further improve the accuracy of UAV personnel detection in complex scenarios. This approach addresses the problems of the small size of pedestrians and the difficulty of detecting personnel under different backgrounds and illumination conditions in UAV remote sensing images. [PERSON] et al. [25] considered problems such as poor pedestrian detection performance due to the effects of a small percentage of pedestrians in an image and large differences in the aspect ratio of the input image. To overcome these challenges, they proposed the ratio- and scale-aware YOLO (RSA-YOLO) algorithm, which enhances the detection model's pedestrian detection performance in the VOC 2012 comp4, INRIA, and ETH datasets. [PERSON] et al.