## 4 Experiments and Performance
### Experimental Results and Analysis
#### 4.2.1 Experimental Validation of MSS-YOLO-Based Miner Identification
(**a**) Comparison of AP50, (**b**) comparison of AP50-95, (**c**) comparison of Rec, and (**d**) comparison of Pre. The black curve represents the training curve of the original YOLOv8n model, and the red curve represents the training curve of MSS-YOLO. The black line box shows a localized zoom of the curve. To further validate the enhancement in detection performance after model improvement, this paper compared MSS-YOLO with the two-stage algorithm Faster-RCNN [36], the single-stage algorithm SSD, and other mainstream YOLO variants using five quantitative metrics, which are AP50, AP50-95, Recall (Rec), Precision (Pre), and Latency. The validation results are shown in Table 2. At the same time, in order to more intuitively represent the detection results of each algorithm, this paper visualized some of the detection results obtained from the different models, and the collated results are shown in Figure 15.  
As shown in Table 2, compared to SSD, Faster-RCNN, and other mainstream versions of YOLO, MSS-YOLO was able to obtain faster and more stable results on the UCMD dataset under all metrics. Among them, compared with YOLOv11, the values for AP50, AP50-95, and Pre improved by 2.188%, 0.19%, and 1.091%. Although the Rec value was reduced by 0.247%, the inference speed improved by 2.3 ms.  
As can be seen from Figure 15, compared with other mainstream algorithms, MSS-YOLO can obtain higher detection confidence in the close detection task and when there is direct interference to the camera from the miner's lamp, and it can improve the leakage of the other models in the miner detection task at long distances and obtain accurate detection results. In \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **AP50/\%** & **AP50-95/\%** & **Rec/\%** & **Pre/\%** & **Latency (ms)** \\ \hline Faster-RCNN [36] & 0.92451 & 0.54902 & 0.90636 & 0.92210 & 57.0 \\ SSD [18] & 0.86431 & 0.49625 & 0.85257 & 0.85332 & 18.5 \\ YOLOv586 [17] & 0.93164 & 0.68271 & 0.91323 & 0.92800 & 10.3 \\ YOLOv7-tiny [37] & 0.92482 & 0.67733 & 0.91912 & 0.91823 & 8.6 \\ YOLOv8n [31] & 0.93260 & 0.69922 & 0.92015 & 0.94908 & 7.8 \\ YOLOv11n [38] & 0.94727 & 0.70224 & 0.93598 & 0.95077 & 8.9 \\ MSS-YOLO (ours) & 0.96915 & 0.70416 & 0.93351 & 0.96168 & 6.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the results of miner identification and detection and evaluation of integrated mining face under different models. Figure 15: Partial detection results for different models. The highlighted part in the lower right corner is the zoomed-in result of the part in the yellow box. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **MSEE** & **SCDH** & **SPSC** & **AP/50\%** & **AP50-95/\%** & **Rec/\%** & **Pre/\%** & **Latency (ms)** \\ \hline YOLOv8n & \(\times\) & \(\times\) & \(\times\) & 0.9326 & 0.69922 & 0.92015 & 0.94908 & 7.8 \\ YOLOv8n-M & \(\surd\) & \(\times\) & \(\times\) & 0.96062 & 0.70291 & 0.93138 & 0.96012 & 7.5 \\ YOLOv8n-S & \(\times\) & \(\surd\) & \(\times\) & 0.95783 & 0.69933 & 0.92909 & 0.94992 & 5.6 \\ YOLOv8-MS & \(\surd\) & \(\surd\) & \(\times\) & 0.95891 & 0.70180 & 0.93000 & 0.95919 & 6.9 \\ MSS-YOLO (ours) & \(\surd\) & \(\surd\) & \(\surd\) & 0.96915 & 0.70416 & 0.93351 & 0.96168 & 6.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of ablation experiments. the miner detection task with high dust, it can also obtain miner detection results in a more stable way. To a certain extent, it meets the requirements of personnel detection in the fully mechanized mining face, thus further ensuring the positioning of coal mine personnel and supporting the task of identifying those violating the rules and invading abnormal areas. 2.2 Experimental Validation of Personnel Localization Method Based on SGBM Stereo Vision Matching Algorithm + MSS-YOLO  
In order to validate the personnel positioning method described in this paper, we simulated the actual situation of a fully mechanized mining face in a coal mine laboratory and conducted experimental validations.