## 2 Literature Review  
Instance segmentation algorithms based on deep learning can generally be divided into two-stage algorithms and single-stage algorithms. Among the existing instance segmentation methods, most high-precision models are constructed based on the idea of two-stage object detection. SDS (Simultaneous Detection and Segmentation) [11] is the earliest instance segmentation algorithm, which simultaneously realizes object detection and semantic segmentation for the first time. Although it shows simplicity in its structure and is not ideal in segmentation results, it remains fundamental for the subsequent two-stage instance segmentation algorithm. Mask R-CNN [12] is one of the most typical two-stage instance segmentation algorithms, which adds a full convolution branch to Faster R-CNN [13] to generate segmentation results and uses a bilinear interpolation method to improve the segmentation accuracy of pixels in the feature region pooling layer in order to better detect small targets. With further research from [PERSON] et al., improved algorithms based on Mask R-CNN have been continuously proposed, such as Mask Scoring R-CNN based on optimized mask evaluation criteria [14] and PointRent [15], which takes the image segmentation task as a rendering task. Meanwhile, along with the development of instance segmentation algorithms, the Detectron2 proposed by Facebook AI Research [16] provides a platform for researchers to share and download state-of-the-art algorithms, which could simplify researchers' work. Most two-stage instance segmentation algorithms are top-down algorithms, following the operation logic of first detection and then segmentation. Correspondingly, there are some bottom-up instance segmentation algorithms that first segment and then cluster. SGN proposed by [PERSON] et al. [17] performs pixel clustering from both horizontal and vertical dimensions to find instance compo nents, and finally synthesizes these components into instance masks. SSAP [18] calculates the probability of two pixels hierarchically belonging to the same instance and proposes a cascading graph-partitioning module to generate instances in a coarse-to-fine order. Although the accuracy of these two-stage algorithms is relatively improved, it is still limited in special tasks, thereby making it difficult for these algorithms to further improve the segmentation speed due to their serial operation logic. Compared with two-stage methods, the single-stage detector YOLACT proposed by [PERSON] et al. [19] greatly improves the segmentation speed of the model by running object detection and semantic segmentation in parallel. [PERSON] et al. [20,21] further studied the use of anchor-free designs such as center point mechanisms. Compared with the anchor-based instance segmentation algorithm, anchor-free models remove the limitation of anchors with a higher accuracy. Along with the growing research on instance segmentation algorithms, researchers have also applied the instance segmentation algorithm to the power industry. [PERSON] et al. [22] conducted the detection of insulator status by using Mask R-CNN to detect and segment insulator infrared images with a transfer learning strategy. [PERSON] et al. [23] embedded an attention mechanism in the encoding stage of U-Net, improving the accuracy of insulator monitoring. [PERSON] et al. [24] used the foreground segmentation of RGB-T images of power equipment to enhance the mask extraction of regular images, realized automatic mask annotation, and improved the efficiency of data annotation. [PERSON] et al. [25] proposed an insulator infrared image segmentation algorithm based on dynamic masks and box annotation, which alleviates the problems of inaccurate positioning, low recognition efficiency, and segmentation difficulties in insulator images against complex backgrounds. Researchers have also carried out corresponding research on the task of insulator defect detection and segmentation. [PERSON] et al.