# Robust Miner Detection in Challenging Underground Environments: An Improved YOLOv11 Approach
## 3 Experiment and Results
### Results and Analysis
#### 3.3.3 Comparison Under Challenging Visual Conditions  
The test images were subjected to reduced brightness, set to half of their original value, and blurring to further validate the robustness and detection performance of the models in complex environments. The blurring operation uses Gaussian blur, and the size of the Gaussian blur kernel is \(7\times 7\). These enhanced testing conditions allow for an evaluation of the models' performance under challenges such as low illumination and image blurring, providing a more comprehensive understanding of their generalization ability and stability across different image qualities. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Precision** & **Recall** & **mAP50** & **FPS** \\ \hline YOLOv11\_PE-YOLO & 0.920 & 0.932 & 0.946 & 10.1 \\ YOLOv11\_Retinexformer & 0.921 & 0.928 & 0.946 & 16.8 \\ YOLOv11\_RepViT & 0.926 & 0.933 & 0.947 & 24.7 \\ YOLOv11\_SwinTransformer & 0.919 & 0.933 & 0.949 & 30.4 \\ Ours & 0.932 & 0.934 & 0.958 & 59.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of test set performance for different backbone architectures. Figure 7: Validation set evaluation metrics for object detection with different backbone architectures. The comparison between Tables 1 and 3 reveals the impact of different attention mechanisms on object detection performance under normal and challenging conditions (brightness halved). Table 3 represents performance under halved brightness conditions; precision, recall, and mAP50 values for all methods decreased, reflecting the difficulty of detecting objects under low-light conditions. However, the proposed method still demonstrated outstanding performance, maintaining an mAP50 of 0.958 without any degradation compared to normal conditions, indicating strong robustness to changes in brightness. YOLOv11_CBAM achieved the highest recall (0.918) among other models, although its precision and mAP50 slightly declined compared to normal conditions. YOLOv11_D-LKA maintained a high precision (0.934) despite a drop in recall to 0.904, suggesting that its accuracy remained strong, but its sensitivity to detecting all instances decreased under lower brightness. Overall, the comparison results indicate that the proposed method is not only effective under normal conditions but also demonstrates high robustness in challenging lighting environments, maintaining higher detection accuracy compared to other attention mechanisms. The comparison between Tables 2 and 4 highlights the impact of different backbone architectures on object detection performance under normal and challenging conditions (brightness halved). In Table 4, which presents the performance of different backbone architectures under halved brightness conditions, it can be observed that most methods experienced a decrease in both recall and precision, reflecting the challenges posed by low-light conditions. However, \"our model\" maintained the highest mAP50 (0.958), indicating its strong robustness to changes in brightness. Compared to Table 2, the precision of \"our model\" slightly decreased from 0.932 to 0.929, but it maintained a high recall (0.930), suggesting that it is less affected by low-light conditions compared to other models. In contrast, YOLOv11_PE-YOLO showed a slight increase in precision (0.931) but a small decrease in recall (0.929), while YOLOv11_Retinexformer, YOLOv11_RepViT, and YOLOv11_SwinTransformer all exhibited declines in both precision and recall, indicating their sensitivity to reduced brightness. Overall, the results from these two tables indicate that \"our model\" not only provides superior accuracy and efficiency under normal conditions but also maintains its robustness under challenging lighting conditions, outperforming other backbone architectures in low-light scenarios.