# Robust Miner Detection in Challenging Underground Environments: An Improved YOLOv11 Approach
## 3 Experiment and Results
### Results and Analysis
#### 3.3.4 Comparison with State-of-Art Methods  
The comparison in Table 7 highlights the performance differences between the proposed model and state-of-the-art object detection methods. The proposed model achieved the highest mAP50 (0.958), indicating superior detection accuracy compared to other methods. Although YOLOv8 had a slightly lower mAP50 (0.954), it achieved the highest frames per second (FPS) value of 62.5, demonstrating better real-time performance. Nevertheless, the FPS of the proposed model (59.6) was also very competitive, indicating a good balance between accuracy and efficiency. SSD and YOLOX performed well in terms of recall, with YOLOX achieving the highest recall (0.949). However, both models had lower precision and mAP50 values compared to the proposed model, suggesting that while these models excel in detecting more objects, their accuracy in correctly classifying detections is slightly inferior. Faster RCNN exhibited relatively low FPS (18.6), highlighting its limitations in processing speed, although its precision (0.886) and recall (0.936) were relatively competitive. YOLOv8 showed a balanced overall performance, with a precision of 0.927 and a recall of 0.929, reflecting a balance between detection accuracy and speed, but it still fell short of the proposed model in terms of mAP50. Overall, the proposed model outperformed state-of-the-art methods in terms of accuracy (mAP50) while maintaining competitive FPS, demonstrating an effective balance between high detection accuracy and efficiency. This balance makes the proposed model highly suitable for practical applications that require both high detection accuracy and fast processing speed.  
To further validate the superiority of \"Ours\", Figure 8 visualizes the detection results under challenging conditions such as low illumination, strong light interference, and severe occlusion. The blue bounding boxes are used to mark coal mine workers who were not detected by the algorithm, highlighting instances of missed detection. As shown in the figure, the proposed algorithm achieves the best detection results across various scenarios, with no false positives or missed detections. Figure 8a illustrates the detection performance under a complex situation involving simultaneous low illumination, strong light, and occlusion, where both YOLOX and YOLOv8 exhibit missed detections. Figure 8b,c present the detection results under low illumination and severe occlusion, where YOLOv8 misses detections in both Figure 8b,c, while YOLOX fails in Figure 8c. These results highlight the challenging nature of underground coal mine environments, which are often characterized by low illumination, headlamp interference, and occlusions from equipment and personnel, all of which can degrade the feature information of miners, resulting in missed detections by these algorithms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Precision** & **Recall** & **mAP50** & **FPS** \\ \hline SSD & 0.916 & 0.912 & 0.920 & 56.5 \\ YOLOX & 0.897 & 0.949 & 0.947 & 29.8 \\ Faster RCNN & 0.886 & 0.936 & 0.920 & 18.6 \\ YOLOv8 & 0.927 & 0.929 & 0.954 & 62.5 \\ Ours & 0.932 & 0.934 & 0.958 & 59.6 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison with state-of-the-art object detection methods.