# HS-YOLO: Small Object Detection for Power Operation Scenarios
## 3 Results and Discussion
### Experiments on the Power Operation Dataset
#### 3.4.2 Ablation Experiment  
To assess the efficacy of the improvements introduced in the HS-YOLO algorithm for small object detection, ablation experiments were performed on each enhancement point using the created dataset of power operation scenes. The results are shown in Table 2. In the table, Experiment 1 represents the results of the original YOLOv5 model. \"A\" indicates the improvement of using the HRNet network as the backbone, while \"B\" represents the improvement of using sub-pixel convolution as the upsampling operator in the feature fusion network.  
Comparing Experiment 2, Experiment 3, and Experiment 4 with the original YOLOv5 network, we can observe that using the HRNet feature extraction backbone network only sacrifices 1% of the detection accuracy while improving the detection recall by 4.1%. Introducing sub-pixel convolution in the Neck leads to a 2% increase in detection accuracy and a 3.1% increase in detection recall. When the original network is improved using HRNet and sub-pixel convolution at the same time, the detection accuracy is improved by 1%, and the detection recall is improved by 3.9%. This indicates that the algorithm can identify objects more accurately and comprehensively, reducing false detections and missed detections.  
HRNet's multi-scale features enable a more comprehensive capture of object contextual information, while sub-pixel convolution upsampling can restore finer spatial details. This aids in reducing false detections and missed detections caused by background interference or blurring. HRNet is more complex than the original backbone network, the introduction of HRNet will bring relatively high computational costs. However, we can observe that improving model detection performance only sacrifices a small amount of FPS. In high-risk power operation scenarios, detection accuracy often takes precedence over speed.  
The experiments in Table 3 show that when HRNet is used to improve the backbone network, only the AP of the Fence category decreases by 3.4%. Other objects showed slight improvements, and objects with consistently small proportions saw significant improvements in AP, with increases of 2.3%, 4.3%, and 11.8% for different objects. The mAP improved by 2.7% compared to before the improvement. After adding sub-pixel convolution to the original network, only the AP of the Human object slightly decreased, while other object categories showed varying degrees of improvement in AP. The mAP improved by 1% compared to before the improvement. When both HRNet and sub-pixel convolution were added to the network, the mAP reached 87.2%, a 3.5% improvement over the original network. There were improvements in AP for all seven object categories, with significant improvements of 4.4%, 5.8%, and 10.7% for the Safetybelt, Noneckline, and Nocuff objects, respectively. This validates that HS-YOLO can be applied to detection tasks in the context of power operation scenarios.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Order** & **A** & **B** & **Recall (\%)** & **Precision (\%)** & **mAP (\%)** & **FPS (\#)** \\ \hline \hline
1 & & & 90.0 & 93.8 & 83.7 & 47.8 \\
2 & \(\surd\) & & 89.0 & 97.9 & 86.4 & 41.7 \\
3 & & \(\surd\) & 92.0 & 96.9 & 84.7 & 48.3 \\
4 & \(\surd\) & \(\surd\) & 91.0 & 98.7 & 87.2 & 42.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation Experiment Results.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Order** & **A** & **B** & **Human (\%)** & **Hat (\%)** & **Safetybelt (\%)** & **Seine (\%)** & **Fence (\%)** & **Noneckline (\%)** & **Nocuff (\%)** \\ \hline
1 & & & 94.3 & 96.5 & 86.8 & 68.0 & 94.1 & 85.1 & 60.9 \\
2 & \(\surd\) & & 95.0 & 97.8 & 89.1 & 70.3 & 90.7 & 89.4 & 72.7 \\
3 & & \(\surd\) & 93.3 & 97.2 & 86.9 & 72.4 & 94.3 & 86.1 & 62.5 \\
4 & \(\surd\) & \(\surd\) & 94.9 & 97.6 & 91.2 & 69.4 & 94.5 & 90.9 & 71.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of various types of AP in ablation experiments.