## 4 Experiments and Performance
### Experimental Results and Analysis  
#### 4.2.1 Experimental Validation of MSS-YOLO-Based Miner Identification  
In order to verify the stability and generalization ability of MSS-YOLO for the detection of miners in a coal mining face under different scenarios, this paper utilized a ZED-2i camera to collect a total of 832 personnel images captured at different distances and under different environmental conditions within a simulated tunnel in the coal mine laboratory of Xi'an University of Science and Technology. These images constitute the UCMD test set for model and method validation. In these photos, in order to simulate the actual working conditions of a fully mechanized mining face, a smoke maker, low light, and a mining lamp were used to simulate the real roadway conditions of an underground coal mine. The miner detection results in the fully mechanized mining face were quantitatively analyzed using four commonly used evaluation indexes: Precision (_Pre_), Recall (_Rec_), AP50, and AP50-95, where AP50 and AP50-95 refer to the average of the Pre metrics calculated for an IoU threshold of 0.5 and from 0.5 to 0.95 in steps of 0.05, respectively. The variation curves of the performance evaluation metrics of MSS-YOLO and YOLOv8n trained on the UCMD dataset are shown in Figure 14, and the calculation formulas are shown in Equations (7) and (8) [35], respectively. \[Pre=\frac{TP}{TP+FP} \tag{7}\]  
\[Rec=\frac{TP}{TP+FN} \tag{8}\]  
Figure 13: Flowchart of 3D spatial solution for underground personnel in the fully mechanized mining face. In the formulas, _TP_, _FP_, and _FN_ represent the true positives, false positives, and false negatives of the inference results of the miner detection model, respectively. In this paper, _TP_ represents the total number of miners correctly detected by the model, _FP_ represents the total number of miners incorrectly detected by the model as miner targets in the underground coal mine, and _FN_ represents the total number of miners not detected by the model in the underground coal mine. As can be seen from Figure 14, the AP50, AP50-95, Rec, and Pre metrics of MSS-YOLO converge to 0.96915, 0.70416, 0.93351, and 0.96168, respectively. These values are higher than those of YOLOv8n, which demonstrates that the improved model is able to better satisfy the demand for personnel identification in the integrated mining face of the underground coal mine. In order to further verify the effectiveness of MSS-YOLO's improvement of each module of YOLOv8n, a set of ablation experiments were designed in this paper to analyze the effectiveness of the model's improvement. Taking YOLOv8n as the benchmark model, the results of the ablation experiments are shown in Table 1, where \"/\" indicates that the module is used and \"\(\times\)\" indicates that the module is not used. As can be seen from Table 1, relative to the YOLOv8n benchmark model, MSS-YOLO improves the AP50 metric by 3.655%, AP50-95 by 0.494%, Rec by 1.336%, and Pre by 1.26% and reduces the inference time by 1.2 ms. It is demonstrated that the MSEE module is able to pay attention to the target edges and the local feature information of targets of different sizes and scales when detecting in the poor environment of the fully mechanized mining face in the coal mine. This ensures that the detection of large targets will not be lost, while also improving the accuracy of the detection of small targets. It is also demonstrated that the introduction of the SCDH module ensures that the model utilizes the shared convolution to reduce the number of parameters and better improve the inference speed of the model. At the same time, it is shown that the SPSC module is able to reduce the model redundancy in a significant way, capture the detailed features in the image effectively, and better improve the model's detection performance for dust and targets at long distances. Figure 14: Variation curves of performance evaluation metrics for MSS-YOLO vs. YOLOv8n trained on the UCMD dataset.