## 3 Experimental Research and Analysis
### Model Experiments and Analysis
#### 3.2.2 Ablation Experiments  
This ablation experiment verifies the effects of HetConv, ShuffleAttention, and InnerSIoU on two different datasets.  
From Table 5, compared with the baseline YOLO11, HSS-YOLO enhanced the accuracy and stability of object detection. First, the HSS-YOLO model reduced parameters by 10.7% and GFLOPs by 9%, making the model more lightweight and easier to deploy and run on resource-limited devices (such as inspection robots). Second, it improved precision by 4.6%, recall by 2.8%, and [EMAIL_ADDRESS] by 4%, significantly increasing detection of small targets and key targets in complex scenarios, proving that the model has high accuracy and stability for key target detection in dim environments. The FPS reached 92, meeting real-time detection requirements.  
YOLO11 + HetConv uses the lightweight HetConv, which reduces the number of parameters by at least 40% compared to the original Conv. From Table 5, the introduction of the HetConv module not only reduced the number of parameters and computation of the model, but also improved the precision by 2.5%, which improved the detection of critical targets.  
YOLO11 + ShuffleAttention, which adds a ShuffleAttention layer before the C2 PSA of the backbone network, is a module that not only enhances the performance of the deep convolutional network when it comes to the detection of key targets, but also enables the network to pay more attention to the important features in the image while suppressing irrelevant information by introducing an attention mechanism in the neural network.  
YOLO11 + Inner-SIoU greatly improved precision by 3.8% and [EMAIL_ADDRESS] by 4.3%. It retained the basic properties of IoU while improving the evaluation accuracy of overlapping bounding boxes through a new calculation method. Inner-SIoU provided finer evaluation capability for the model and enabled more accurate recognition and localization of targets in complex scenes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Model** & **Parameters** & **GFlops** & **Precision** & **Recall** & **[EMAIL_ADDRESS]** & **FPS** \\ \hline YOLO11 + Custom Dataset & 2.59 M & 6.4 & 0.859 & 0.787 & 0.836 & 100 \\ \hline HSS-YOLO + Custom Dataset & 2.29 M & 5.8 & 0.905 & 0.815 & 0.876 & 92 \\ \hline YOLO11 + DoorDetect Dataset & 2.59 M & 6.4 & 0.932 & 0.902 & 0.949 & 100 \\ \hline HSS-YOLO11 + DoorDetect Dataset & 2.59 M & 6.4 & 0.945 & 0.931 & 0.962 & 105 \\ \hline \end{tabular}
\end{table}
Table 4: Comparative test.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Model** & **Parameters** & **GFlops** & **Precision** & **Recall** & **[EMAIL_ADDRESS]** & **FPS** \\ \hline YOLO11 & 2.59 M & 6.4 & 0.859 & 0.787 & 0.836 & 100 \\ \hline YOLO11 + HetConv & 2.29 M & 5.8 & 0.884 & 0.791 & 0.848 & 90 \\ \hline YOLO11 + ShuffleAttention & 2.59 M & 6.4 & 0.864 & 0.796 & 0.843 & 100 \\ \hline YOLO11 + Inner-SIoU & 2.59 M & 6.4 & 0.897 & 0.834 & 0.879 & 105 \\ \hline HSS-YOLO & 2.29 M & 5.8 & 0.905 & 0.815 & 0.876 & 92 \\ \hline \end{tabular}
\end{table}
Table 5: Improved module comparison.  
Finally, HSS-YOLO achieved the best results in this experiment, with improvements of 4.9% precision, 2.8% recall, and 4% [EMAIL_ADDRESS], indicating that the model provides higher accuracy.  
Table 6 shows that HSS-YOLO maintains the same advantages in ablation experiments on the public dataset as on the custom dataset, improving its detection capability.  
Overall, the experimental and data analyses of each module demonstrated that HSS-YOLO greatly strengthened detection ability for key targets. Subsequent experiments also proved that the model had strong recognition capability in dim environments of power distribution rooms, enhancing the model's generalization and accuracy.