# HS-YOLO: Small Object Detection for Power Operation Scenarios
## 4 Conclusions  
Various factors influencing the detection of small objects in electric power operation scene monitoring include small object pixel size, complex background information, and image blurring. To address the challenges of small object detection in power operation scenarios, this paper proposes the HS-YOLO object detection algorithm based on HRNet and sub-pixel convolution. HRNet is utilized to extract features from small objects, alleviating the problem of significant feature loss during extraction. During the multi-scale feature fusion, sub-pixel convolution is employed to upsample the low-resolution feature maps, preserving more feature information of small objects. Experimental results indicate that compared to other classical methods, HS-YOLO performs better on various object categories. With the same number of training epochs, the proposed HS-YOLO achieves a mAP of 87.2% on the self-constructed electric power operation scene dataset, which is a 3.5% improvement over YOLOv5. Particularly, there is a notable improvement in the detection of small objects in the dataset. HS-YOLO greatly reduces the occurrence of missed detections for small objects in power operation scenarios compared to YOLOv5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **AP\({}^{\text{small}}\)(\%)** & **AP\({}^{\text{medium}}\)(\%)** & **AP\({}^{\text{large}}\)** & **mAP (\%)** \\ \hline YOLOv5 & 18.5 & 39.3 & 44.8 & 53.6 \\ HS-YOLO & 27.1 & 48.6 & 56.2 & 63.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Detection of objects of different sizes in the COCO dataset by Hs-YOLO and YOLOv5.  
Figure 8: The training loss comparison between HS-YOLO and the original YOLOv5 on the COCO dataset. (**a**) represents a comparison in total training loss; (**b**) represents a comparison in confidence loss; (**c**) represents a comparison of classified loss; (**d**) represents a comparison in bounding box regression loss.  
In future work, considering the practical deployment and application of the algorithm, we will further focus on lightweighting the algorithm and conduct research on real-time detection.  
Conceptualization, Z.L., W.C. and L.S.; methodology, Z.L. and W.C.; software, Z.L.; validation, Z.L., W.C. and Y.C.; formal analysis, Z.L.; investigation, W.C. and T.L.; resources, Z.L. and W.C.; data curation, Z.L.; writing--original draft preparation, Z.L.; writing--review and editing, Z.L.; visualization, Z.L.; supervision, L.S. and T.L.; project administration, Z.L.; funding acquisition, L.S. and T.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of the Department of Science and Technology of Fujian Province under Grant No. 2022J011255 and the Science and Technology Project of East China Branch of State Grid under Grant No. SGHD0000 AZJS2310287.  
Not applicable.  
Data Availability Statement: Data is not available for privacy reasons.  
We thank Fujian Xiamen State Grid Corporation for the photos of power operators and FanYin of the State Grid East China Branch provided valuable help.  
The authors declare no conflict of interest.