## 1 Introduction  
Due to external erosion and internal overload during long-term operation, various faults will inevitably occur in insulators, and the stability of the power grid will also be affected by insulator defects. Regular inspection of the insulator's operation in advance allows for the detection of equipment problems to a certain extent, thereby reducing the damage to the power grid caused by the insulators [1]. With traditional manual inspection finding it difficult to meet the increasing efficiency requirements in the power industry, an important trend is using robots for the promotion of power equipment inspection [2]. The replacement of manual inspection by automatic inspection is advantageous in reducing labor consumption, increasing the efficiency of inspection, and ensuring the stable operation of the power grid. The instance segmentation algorithm is one of the important technologies for inspection robots to obtain power equipment information quickly and accurately. This algorithm divides the image into several regions with unique properties and then extracts the target of interest from these regions. Compared with the bounding box extracted by the object detection algorithm, the instance segmentation algorithm has a more refined performance on the target boundary and removes redundant background information, making the algorithm more conducive to the acquisition of the target instance information.  
In complex situations such as target overlapping, the instance segmentation algorithm is more accurate in extracting the target information and more suitable for the accurate monitoring of power equipment, and it is an effective technical means for automatic inspection robots to identify and segment power equipment [3]. The targeted improvement, transplantation, and application of the instance segmentation algorithm in the power industry improves the accuracy of automatic inspection and enhances the ability to locate equipment faults in advance.  
The instance segmentation algorithm has achieved many satisfactory results in traditional sectors such as transportation, agriculture, and medicine, but there are still many problems in some specific situations, including the occlusion between instances, the difficulty in detecting small instances, and the lack of data samples [4; 5; 6]. Especially in the case of insulator defect segmentation, there are characteristics such as a small target scale and large number of targets that lead to a low accuracy of those instance segmentation methods when applied to insulator image segmentation, while the speed is also limited by the number of targets [7]. These factors limit the accuracy and speed of instance segmentation algorithms. In order to better segment insulator images, an improved YOLACT algorithm for the detection and segmentation of insulator defects is proposed in this paper. Firstly, we modify the backbone network of MobileNetV2 [8] embedded with an scSE (Concurrent Spatial and Channel Squeeze & Excitation) mechanism [9] to improve the inference speed of the model. Secondly, the ECA-Net (Efficient Channel Attention Network) [10] module is introduced into the FPN module while the feature fusion method is also improved, so as to optimize the feature map. Finally, a bounding box loss function, RIoU Loss, which covers area deviation, center deviation, and shape deviation is designed to better limit the generation of redundant bounding boxes.  
A literature review will be conducted in Section 2. In Section 3, the overall structure of the improved YOLACT algorithm and the improvements in each module are introduced. Section 4 follows the experiments carried out in this paper, including the data set, experimental environment, and parameter settings, as well as the analysis of the experimental results. In the Section 5, the research results and the prospects for future research are summarized.