# Robust Miner Detection in Challenging Underground Environments: An Improved YOLOv11 Approach
## 2 Materials and Methods  
### Dataset Introduction  
The standardized target detection dataset for the coal mine underground drilling site was gathered between July 2020 and August 2023 in a coal mine located in Binzhou City, Shaanxi Province [18]. An intrinsically safe law enforcement recorder was employed to capture footage of the drilling site. This equipment was capable of acquiring images with a maximum resolution of 4 million pixels, achieving a frame rate of 30 FPS, and the video format used was MPEG-4. The original dataset consists of 976 video clips, with a total duration of 161.8 h. The dataset used in this study was generated by randomly extracting video frames from the original footage at intervals of 30-50 frames using the OpenCV library. After data cleaning, annotation, and expert sampling and verification, the final dataset consists of 10,066 images of coal miners captured from various drilling sites and different environmental backgrounds. During the data cleaning process, we did not specifically address motion blur or low-quality images. This approach was intentional, as we aimed to provide a diverse and authentic dataset for researchers. These images cover three brightness levels (low, medium, and high) and are taken from front, left, and right angles. Additionally, the dataset includes complex scenes, such as target occlusions and blurring. All images have a resolution of 1280 \(\times\) 720, with object scales ranging from 500 to 55,000 pixels. The majority of the targets have scales exceeding 3000 pixels, reflecting significant variation in the target sizes within the dataset, which meets the requirements for multi-scale object detection.  
To enhance the accuracy of the training dataset, it is divided into a training set, validation set, and test set in an 8:1:1 ratio. It is important to ensure that the chosen training, validation, and test sets encompass all possible scenarios. This study employs the LabellImg tool to annotate the image dataset following the VOC format. To ensure high-quality annotations, the following four standards are strictly adhered to during image annotation. (1) Annotations should be made by drawing a box closely around the target's edge. (2) If the target is largely occluded or the overall size cannot be marked, annotations are omitted. (3) Even with minor occlusions, the target must still be annotated, with the obscured part manually added. (4) The annotation box should ideally not be positioned near the image's boundary. Consequently, for the issue of pedestrian detection in coal mines, the experimental results based on this data are more persuasive.  
The sample images of the dataset are shown in Figure 1. In general, the images in the dataset are captured under low-light conditions, with significant challenges such as heavy occlusion and intense light interference caused by headlamps and other light sources. These factors contribute to the complexity and difficulty of accurately detecting miners in this specific environment. To further evaluate the algorithm's adaptability in challenging environments, we applied brightness reduction and blur to the images in the testing dataset, simulating real-world variations and assessing the algorithm's generalization capability.