# HS-YOLO: Small Object Detection for Power Operation Scenarios
## 3 Results and Discussion
### Experiments on the COCO Dataset  
To further provide an impartial and objective evaluation of the generality and generalization of the HS-YOLO algorithm, a comparison was made between the introduced HS-YOLO algorithm and the unaltered YOLOv5 on the publicly available COCO dataset.  
Both HS-YOLO and the original YOLOv5 were trained for 100 epochs on the COCO dataset, and we compared their training processes. The training losses of the two models are shown in Figure 8. The training loss of the original YOLOv5 is depicted by the blue curve, whereas the training loss of the proposed HS-YOLO is indicated by the orange curve.  
As shown in Figure 8, HS-YOLO exhibits lower overall training loss and various class-specific losses compared to YOLOv5 after 100 epochs of training, indicating that HS-YOLO performs better. Moreover, all class-specific losses steadily decrease during training, indicating that the model's performance steadily improves over time.  
In the COCO dataset, the objects are categorized into three size ranges based on their pixel areas. To gain a comprehensive understanding of the algorithm's performance on objects of different sizes, we compared HS-YOLO and YOLOv5 in detecting objects of different sizes on COCO. The results are shown in Table 6, where \(\text{AP}^{\text{small}}\), \(\text{AP}^{\text{medium}}\), and \(\text{AP}^{\text{large}}\) represent the average precision for small, medium, and large objects, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Human** & \multirow{2}{*}{**Hat (\%)**} & **Safetybelt** & \multirow{2}{*}{**Seine (\%)**} & \multirow{2}{*}{**Fence (\%)**} & \multirow{2}{*}{**Nonecline**} & & & \\  & **(\%)** & & & **(\%)** & & & **(\%)** & \\ \hline Faster-RCNN & 86.7 & 82.4 & 64.7 & 63.6 & 87.5 & 78.3 & 56.9 & 74.3 \\ SSD & 85.9 & 80.6 & 65.8 & 60.8 & 88.4 & 79.5 & 54.8 & 73.7 \\ YOLOv3 & 91.2 & 83.4 & 72.9 & 66.3 & 92.7 & 82.1 & 60.1 & 78.4 \\ YOLOv4 & 91.8 & 85.5 & 77.4 & 65.1 & 93.6 & 83.9 & 59.8 & 79.6 \\ YOLOv5 & 94.3 & 96.5 & 86.8 & 68.0 & 94.1 & 85.1 & 60.9 & 83.7 \\ YOLOv8 & 95.3 & 97.4 & 89.5 & 70.5 & 94.9 & 88.4 & 67.3 & 86.2 \\ HS-YOLO & 94.9 & 97.6 & 91.2 & 69.4 & 94.5 & 90.9 & 71.6 & 87.2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative experimental results.  
The experimental results in Table 6 demonstrate that when HS-YOLO is used as the detection network, the mAP is 63.0%, which is an improvement of 9.4% over the unmodified YOLOv5. Furthermore, for objects of different sizes in the COCO dataset, HS-YOLO achieves \(\text{AP}^{\text{large}}\), \(\text{AP}^{\text{medium}}\), and \(\text{AP}^{\text{small}}\) of 56.2%, 48.6%, and 27.1%, respectively. Compared to the unmodified YOLOv5, HS-YOLO increases \(\text{AP}^{\text{large}}\), \(\text{AP}^{\text{medium}}\), and \(\text{AP}^{\text{small}}\) by 11.4%, 9.3%, and 8.6%, respectively. These results indicate that HS-YOLO demonstrates better detection performance on objects of different sizes. Therefore, the proposed HS-YOLO algorithm not only performs well in small object detection in power operation scenes but also exhibits significant advantages on the COCO dataset. This demonstrates that HS-YOLO possesses strong generality and generalization capabilities.  
Figure 7: The detection results of the method before and after improvement on the power operation scenarios dataset. (**a**) the detection results of the YOLOv5 model; (**b**) the detection results of the HS-YOLO model.