## 1 Introduction  
With the development of the power industry, the safe operation of electrical facilities and equipment has become a focal point for both the industry and the public. In particular, surface defects on the insulators of power transmission lines are a significant contributing factor to transmission line failures. These surface defects could cause a degradation in insulator performance, which will lead to flashovers, system paralysis, or even power system accidents, presenting a serious threat to the safe operation of power systems [1]. To prevent such scenarios, the regular inspection of ceramic insulators on transmission lines plays a pivotal role in grid transmission [2, 3]. In the past few years, many scholars have conducted a series of research on insulator surface defect detection. The traditional detection methods mainly categorize into power-based, microwave-based, infrared imaging-based, and other electromagnetic wave technology-based approaches. [PERSON] et al. [4] computed the electric field distribution of normal insulators and insulators with conductive defects and analyzed the influence of defect length and position parameters on insulator electric field distribution. [PERSON] et al. [5] proposed a microwave technology-based method for detecting internal defects of composite insulators. [PERSON] et al. [6] introduced a defect detection method that converted reflected microwaves into energy signals and compared them with reference values. [PERSON] et al. [7] introduced a strategy for intermediate feature generation based on binary feature pooling,which combined with support vector machine (SVM) effectively detects and classifies insulator string defects in infrared images. [PERSON] et al. [8] used the infrared detection for online analysis of composite insulators to determine the relationship between defect temperature and infrared results with different types of thermal defects for composite insulators. [PERSON] et al. [9] used a terahertz time-domain system to test insulator models with different interface defects and analyze the characteristics of time-domain waveforms. [PERSON] et al. [10] proposed a terahertz (THz) imaging method based on edge detection algorithms. [PERSON] et al. [11] proposed an ultrasonic non-destructive testing method for assessing the density uniformity of basin insulators in gas-insulated metal-enclosed switchgear devices. But in practical application, these traditional detection methods are often subject to spatial constraints. Especially when inspecting high-altitude insulation equipment, it is difficult for personnel to directly contact the equipment, making these methods relatively low in feasibility and efficiency. Additionally, due to the vastness of power systems, these methods also entail significant time and labor costs, which is especially prominent in comprehensive inspections of large-scale power grids. Therefore, insulator surface defect detection methods based on image recognition have got more and more attention. Insulator image processing methods can be divided into two categories. The first one encompasses traditional image processing approaches, which extract insulator features through techniques such as spatial transformations, threshold segmentation, and morphological filtering. [PERSON] et al. [12] developed an adaptive neuro-fuzzy inference system based on SVM to estimate the condition of insulators. [PERSON] et al. [13] proposed a method oriented toward angle detection and binary shape prior knowledge. [PERSON] et al. [14] introduced a fusion algorithm based on shed contour features and grayscale similarity matching. [PERSON] et al. [15] put forward a solution for detecting bundle drop faults of glass and ceramic insulators using spatial morphological features. The above methods can yield satisfactory localization results when handling images that possess simple backgrounds, especially for the texture features of impurities are highly contrasted with those of insulators.