# HS-YOLO: Small Object Detection for Power Operation Scenarios
## 3 Results and Discussion
### Experiments on the Power Operation Dataset
#### 3.4.3 Comparative Experiments  
To affirm the dependability of the HS-YOLO network as proposed, we partitioned the training process into three stages, each spanning 100 epochs. Within each stage, we randomly selected an epoch and conducted a comparative evaluation of detection performance between the baseline YOLOv5 network and the HS-YOLO network we proposed. We selected the 100 th epoch, 200 th epoch, and 300 th epoch for the comparative analysis of the three training stages. As shown in Table 4, after the first stage of training, both the YOLOv5 and HS-YOLO networks achieved near-optimal levels of AP for detecting Human, Hat, Seine, and Fence in the dataset, with little difference between them. However, for the Safetybelt, Noneckline, and Nocuff classes (all of which are all small objects), the AP can be greatly improved. In the Epoch 100 experiment, both HS-YOLO and YOLOv5 had lower maps, but HS-YOLO outperformed YOLOv5 with a 5.2% higher mAP. Specifically, Noneckline and Nocuff categories had significantly improved AP, with increases of 10.3% and 24%, respectively. By the 200 th epoch, after sufficient training, both HS-YOLO and YOLOv5 achieved high mAP values. However, HS-YOLO had a higher mAP of 83.4%, surpassing YOLOv5 by 5.3%. Notably, the Safetybelt, Noneckline, and Nocuff categories exhibited improved AP, with gains of 5.2%, 10.6%, and 19.8%, respectively. In the 300 th epoch, HS-YOLO maintained its superiority with a 2.8% higher mAP than YOLOv5. Moreover, the Safetybelt, Noneckline, and Nocuff categories showed AP improvements of 3.8%, 3.7%, and 9.4%, respectively. Throughout the entire training stage, our proposed HS-YOLO consistently outperformed YOLOv5 in detecting various objects in the power operation scenarios, especially small objects. HRNet processes images using a multi-resolution approach and is able to capture multi-scale features, which helps to retain details of small objects and provide richer feature representations. Sub-pixel convolution performs finer upsampling on the feature, and aids in achieving more precise object boundary localization. This significantly enhances the precision of detection, enabling the detection model to precisely recognize and pinpoint small objects. To further evaluate the HS-YOLO algorithm, we conducted experiments on the power operation scene dataset and compared it with other classic algorithms. The results in Table 5 demonstrate that our proposed HS-YOLO achieves higher AP for various object categories in the power operation scene compared to the original network and other classical networks. The mAP also shows significant improvement, with a 3.5% increase compared to the original network and a 13.5% increase compared to SSD. Particularly, there is a significant improvement in AP for small objects that are prone to be missed in the electric power operation scene, such as Safetybelt, Noneckline, and Nocuff categories. Even when compared to the latest YOLOv8, HS-YOLO exhibits similar detection accuracy on four large objects of Human, Hat, Seine, and Fence. Moreover, it shows varying degrees of improvement in the detection of three small objects of Safetybelt, Noneckline, and Nocuff, with respective increases in AP of 1.7%, 2.5%, and 4.3%. This \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**100 Epoch**} & \multicolumn{2}{c}{**200 Epoch**} & \multicolumn{2}{c}{**300 Epoch**} \\  & & **YOLOv5** & **HS-YOLO** & **YOLOv5** & **HS-YOLO** & **YOLOv5** & **HS-YOLO** \\ \hline \multirow{8}{*}{AP (\%)} & Human & 93.3 & 93.5 & 93.9 & 94.0 & 94.3 & 94.4 \\  & Hat & 96.3 & 96.7 & 96.7 & 97.2 & 96.7 & 97.6 \\  & Safetybelt & 79.0 & 79.1 & 80.4 & 85.6 & 86.0 & 89.8 \\  & Seine & 63.0 & 64.6 & 66.7 & 67.0 & 68.3 & 68.8 \\  & Fence & 91.8 & 91.8 & 91.4 & 92.6 & 93.7 & 94.7 \\  & Noneckline & 67.9 & 78.2 & 73.5 & 84.1 & 85.8 & 89.5 \\  & Nocuff & 18.6 & 42.6 & 43.8 & 63.6 & 62.2 & 71.6 \\  & mAP (\%) & 72.9 & 78.1 & 78.1 & 83.4 & 83.8 & 86.6 \\ \hline \hline \end{tabular}
\end{table}