# HS-YOLO: Small Object Detection for Power Operation Scenarios
## 3 Results and Discussion  
### Dataset  
There are currently no publicly available data sets for power operation scenarios. To evaluate the efficacy of HS-YOLO for detecting small objects in power operation scenarios, a custom dataset suitable for power operation scenes was constructed in this study.  
(1) Data Collection: The images in the power operation dataset were collected from on-site inspections in a substation using two methods: photographs taken by inspection personnel and the images captured from the actual power operation scene surveillance video. A total of 6550 images were collected, capturing different angles of power operation scenes.  
In the collected dataset, the dimensions of objects within the monitoring images exhibited variation in response to alterations in distance or shooting angles between the objects and the camera. Based on the variations in object sizes present in the monitoring images, the objects are divided into two categories: (1) Small objects that consistently maintain a diminutive presence within the image regardless of the distance and angle. As shown in Figure 5a,b, the object of detecting the neckline of a work uniform is considered a small object in the monitoring images regardless of the shooting angle and distance. (2) Objects whose size in the image varies greatly as the distance and angle shot change. As shown in Figure 5c,d, when the object of detecting a safety seine is close to the monitoring camera, it occupies a large proportion of the monitoring image and is considered a medium-to-large object. However, when the object is far from the monitoring camera, it occupies a small proportion of the monitoring image and is considered a small object.  
(2) Data Cleaning, Augmentation, and Normalization: Firstly, the collected image data was cleaned by removing images that had no objects, severe object occlusions, or excessive blurriness, as these images would not contribute to effective model training. Secondly, diverse data augmentation methods, including but not limited to blurring, rotation, and Gaussian noise, are used to process the cleaned images, and the data set is further expanded. The expanded data set of plugging power operation scenarios contains 4950 images. Finally, the pixel sizes of the images obtained by different methods were inconsistent. To ensure consistent input for the model training, the image sizes were normalized. The commonly used input size for YOLOv5 is 640 \(\times\) 640 pixels. However, the power operation scene images we collected mainly had three types of sizes: 5184 \(\times\) 3888, 1280 \(\times\) 720, and 1920 \(\times\) 1080. In this experiment, the image sizes were normalized to 1280 \(\times\) 800 pixels to expedite the model training process.  
(3) Data Annotation and Dataset Split: Based on the detection task described in Section 1, we used the LabelImg 1.8.3 software to annotate seven detection objects in the images of the power operation scene dataset. The annotated objects include Human, Hat, Safetybelt, Seine, Fence, Noneckline, and Nocuff. According to the proportion of the objects in the images, we divided the seven objects into two categories: objects with small proportions and objects with large proportion spans. The objects with small proportions include Safetybelt, Noneckline, and Nocuff, while the objects with large proportion spans include Human, Hat, Seine, and Fence. The number of annotations for each category in the data set is shown in Table 1.  
Figure 5: Object size under different shooting distance and angle. (**a**,**b**) represents objects that have always been small in the monitoring image; (**c**,**d**) represents objects that vary greatly in scale in the surveillance image.