## 3 Methods
### Underground Coal Miner Dataset  
In order to realize the task of identifying and locating personnel in a fully mechanized mining face underground in coal mines, we combined part of the miner class data in the open dataset DsLMF+, which is our previous open dataset for intelligent identification and classification of anomalies in underground coal mines, and the data of the simulated miners in the Coal Mining Main Laboratory of Xi'an University of Science and Technology (a total of 7675) as the image data. The sensor used to collect the analog miner data was a ZED2i stereo vision camera with a resolution of 1920 \(\times\) 1080, a baseline of 120 mm, a lens focal length of 4 mm, and a field of view of up to 72\({}^{\circ}\) (H) \(\times\) 44\({}^{\circ}\) (V) \(\times\) 81\({}^{\circ}\) (D). In order to ensure the detection stability of the training model in different scenarios and avoid overfitting of the model, the images of the miners in the fully mechanized mining face collected in this dataset contain several different scenarios: low concentration of dust, high concentration of dust, dark environment, overexposed environment, uneven light environment, different distances, and different personnel densities. Some of the acquired images are shown in Figure 10.  
In order to further improve the construction of the dataset, the unlabeled images of miners in the simulated data of the Coal Mining Main Laboratory of Xi'an University of Science and Technology (XUST) were manually annotated using the Labelme tool (5.3.1) ([[https://github.com/labelmeai/labelme](https://github.com/labelmeai/labelme)]([https://github.com/labelmeai/labelme](https://github.com/labelmeai/labelme)), accessed on 10 September 2023), and the annotated label file format was in YOLO format, with the labels named as Coal_miner. Subsequently, the image data as well as the labeled files were divided in a ratio of 8:2, with the training set containing 6140 sheets and the validation set containing 1535 sheets. The dataset was named UCMD. Meanwhile, to further elaborate the data distribution, this paper summarizes the scatter distribution of the width and height of the normalized bounding box among all the artificial labels in the UCMD dataset. The distribution is shown in Figure 11.  
Figure 10: Images of some of the underground personnel captured. From the scatter distribution of the width and height of the bounding box after the normalization of the UCMD label file in Figure 11, the distribution of the miner label (Coal_miner) is more comprehensive. The width of most of the bounding boxes accounts for about 5% to 10% of the total width of the image, and the height of most of the bounding boxes accounts for about 20% of the total height of the image. There are also some extremely large bounding boxes and very small bounding boxes, which proves the reasonableness of the construction of the UCMD dataset and enables the model to have a certain degree of generalization ability, as well as reducing the risk of overfitting during the training of the network. At the same time, in order to calculate the different distributions of the image data in different environments and conditions, we invited three people from the team who have experience of working in underground coal mines to determine the conditional distribution of the UCMD dataset. If divided based on dust concentration, the total number of images with a high dust concentration is 836, equal to 10.9 percent of the total, and the total number of images with a dust-free or normal dust concentration environment is 6839, or 89.1 percent of the total. If divided by light brightness, the total number of overexposed images is 511 (6.7%), the total number of images in low light is 494 (6.4%), and the total number of images in normal light is 6670 (86.9%).