## 4 Results
### Experimental Data  
Since there is currently no publicly available dataset for detecting the open and closed states of isolator switches, we needed to construct a dataset for algorithm training and evaluation. During the dataset construction process, we carried out a significant amount of work, including image collection, image augmentation, and annotation. In this paper, we obtained a set of isolator switch open and closed state images from actual substation operation and maintenance environments. The collected images were cleaned and annotated to create a dataset suitable for this study. The specific implementation was as follows:  
Firstly, we collected a large number of high-voltage isolator switch images from real substations, totaling 1654 images. The images were captured using high-resolution cameras installed within the substations, with resolutions of \(704\times 576\) and \(1280\times 720\). Typically, the images were taken from a low-angle view. These images feature various types of isolator switches and cover a range of angles and distances, both near and far, as well as different sizes. The dataset includes images under various lighting conditions, weather environments, and times of day, including morning, noon, and evening. The weather conditions represented include cloudy, foggy, and sunny days, as shown in Figure 6.  
Secondly, we performed data cleaning and augmentation on the collected data, as some images were blurry, incomplete, or duplicated. Before annotation, we cleaned the dataset by removing invalid or unclear images. To enhance the robustness and generalization of the training algorithm and improve its performance under noise, environmental changes, and other anomalies, we applied data augmentation techniques during training. These  
Figure 6: The self-constructed isolator switch dataset includes various angles and weather conditions. (**a**) Represents the closed state of a horizontal telescopic isolator switch, (**b**) represents the open state of a horizontal telescopic isolator switch, (**c**) represents the open state of a vertical telescopic isolator switch, and (**d**) represents the closed state of a vertical telescopic isolator switch. We have the object to be detected with a yellow box.  
techniques included the following: Scaling and - piping: By scaling and - piping the images, we generated new training samples. Lighting adjustments: We modi ed the hue and saturation of the images to simulate various lighting conditions, helping the model adapt to real-world changes in lighting. Blur effects: We added blurred images to improve the model's robustness in noisy environments. Mosaic augmentation: We combined four training images into one, simulating different scene compositions and object interactions, thereby enhancing the model's ability to recognize complex scenarios.  
Finally, we used the Labellmg 1.8.6 software to annotate six objects in the power equipment dataset. These annotations covered three common types of isolator switches: single-arm horizontal telescopic isolator switches, single-arm vertical telescopic isolator switches, and double-arm vertical telescopic isolator switches. Each type of switch image was further divided into two states: open and closed. We annotated the images using rectangular bounding boxes, manually marking the position of the isolator switches and assigning corresponding labels based on their open or closed state. After augmenting the dataset, we obtained a total of 4962 images, with 3473 images selected as the training set, 992 as the validation set, and 497 as the test set. Figure 6 shows a sample from the dataset. Speci cally, the dataset was designed to detect the open and closed states of isolator switches.