## 1 Introduction  
The substations and related equipment of the state grid play a crucial role in the power transmission and distribution system, being regarded as core electrical assets. Ensuring the normal operation of this equipment is not only an important task for the power industry but also a key factor in maintaining the stability of the power grid. Since these electrical devices are exposed to natural environments for extended periods, they face challenges from extreme weather conditions, such as strong winds and heavy rain. These weather phenomena can lead to damage or failure of the equipment, adversely affecting the normal operation of transmission and distribution lines. Therefore, real-time monitoring of the status of electrical equipment has become an indispensable task in power inspections [1, 2, 3]. Compared to traditional manual inspection methods, drone inspections offer significant advantages. Firstly, drones provide high flexibility, enabling them to quickly adapt to complex inspection requirements. Secondly, their high safety standards reduce the risks associated with manual live-line operations. Finally, their cost-effectiveness helps alleviate the high labor intensity caused by the widespread distribution of power lines. These advantages make drone inspections highly valuable in the power industry, as they not only improve inspection efficiency but also ensure the safe operation of the power grid, ultimately contributing to the modernization of the power system [4; 5; 6]. Recent studies primarily focus on using drones to collect inspection images and apply deep learning for detecting the operational status of electrical equipment [7; 8; 9]. [PERSON] et al. [10] primarily presents a novel object detection method by improving the Faster R-CNN algorithm [11]. This involves the integration of the InResNet architecture to boost feature extraction performance, substituting normalization techniques and activation functions, along with implementing a dense connection framework. These modifications lead to a notable enhancement in both the detection accuracy and recall rate for infrared images of electrical devices. Experimental results indicate that the proposed algorithm surpasses conventional detection methods in several performance metrics. [PERSON] et al. [12] also optimizes the Faster R-CNN model, specifically enhancing the feature extraction part of VGG16 by reducing the number of high-level convolutions to accelerate training and testing speeds. Additionally, to improve the model's detection accuracy for elongated devices, it introduces anchors with different aspect ratios. Experimental results indicate that the improved model shows enhancements in both accuracy and operational speed and exhibits robustness against noise and brightness interference. Similarly, [PERSON] et al. [13] introduces an improved object detection model, YOLO-SD, specifically addressing the issues of low accuracy and lack of robustness in detecting equipment defects in substations. Through the integration of the C3+ feature extraction module and the SimAM attention mechanism, along with the development of a specialized fusion loss function, NWD-CIoU, the YOLO-SD model demonstrates exceptional accuracy and robustness in detecting defects in substation equipment, thereby offering robust support for the deployment of intelligent online inspection systems. [PERSON] et al. [14] proposes a method for the recognition of abnormal infrared images of electrical equipment based on YOLOv4. It constructs four datasets of electrical equipment images and analyzes the impact of relevant factors on object detection effectiveness through experiments, ultimately establishing the optimal detection model. The findings reveal that YOLOv4 excels in identifying anomalies within infrared images of electrical equipment and demonstrates a strong potential for application in the infrared inspection of substation devices. Moreover, [PERSON] et al.