## 1 Introduction
However, they have shown limited robustness when dealing with complex backgrounds, large camera dynamics, and low signal-to-noise ratios. Another category is based on deep learning algorithms, which apply object detection algorithms to the task of identifying insulators and their defects. The strength of deep learning methods lies in their ability to handle a variety of complex scenarios and adapt to different image qualities, as they can learn complex features from large amounts of data. [PERSON] et al. [16] proposed an improved small insulator defect detection network to better utilize channel information and enhance the effect of different channels on the feature map. [PERSON] et al. [17] constructed a new localized network called the TOL framework to reduce background and achieve closer localization. [PERSON] et al. [18] used Faster RCNN to locate insulators and extract their target defect images from detected images. [PERSON] et al. [19] proposed an INSU-YOLO insulator detection algorithm based on the adaptive threat estimation method of the whole insulator and defect area ratio. [PERSON] et al. [20] constructed a new network based on ResNeSt and added an improved RPN for feature extraction to better detect minute defects on insulators. [PERSON] et al. [21] improved the YOLOv4 model structure using MobileNet lightweight convolutional neural network for the detection of transmission line insulator defects. [PERSON] et al. [22] proposed an insulator defect detection algorithm based on the improved MobilenetV1-YOLOv4, enhancing the accuracy of insulator fault recognition and the convenience of daily work. [PERSON] et al. [23] presented a SE-YOLOv5 based on a synthetic fog algorithm for insulator detection. [PERSON] et al. [24] proposed an improved SIoU loss function considering the regular influence of regression direction on accuracy, which can accelerate model convergence and achieve better results in regression. [PERSON] et al. [25] studied the detection of power line insulator defects using aerial images analyzed with convolutional neural networks. [PERSON] et al. [26] proposed a convolutional neural network integration with data enhancement and transfer learning for insulator fracture detection with small samples. [PERSON] et al. [27] introduced a motor fault detection scheme based on one-class tensor hyperdisk, which emphasized the ongoing relevance of data-driven methods for fault detection. [PERSON] et al. [28] introduced an end-to-end physics-informed gated recurrent graph attention unit network for unsupervised anomaly detection, highlighting the integration of prior knowledge with process data to improve the modelling performance and interpretability. The above methods based on deep learning are superior to traditional methods, especially in situations involving complex backgrounds or challenging imaging conditions. However, it should be noted that these methods typically require substantial computational resources, which is not ideal for real-time application scenarios and resource-limited environments. Therefore, it is urgent to solve the balance problem between detection speed and detection accuracy, to meet the increasing efficiency and precision requirements of power companies for inspections. In addition, in the context of insulator defect detection, segmentation algorithms aid in precise pixel-level object detection and enhance the semantic understanding of an object's shape and size. Their high accuracy, coupled with their heightened instance-awareness capabilities, makes them an excellent choice for effective detection and quantification of defects. [PERSON] et al. [29] proposed an intelligent defect detection algorithm for the contact network based on the Mask Region Convolutional Neural Network (R-CNN). [PERSON] et al. [30] developed an outdoor support insulator surface defects segmentation approach via image adversarial reconstruction. [PERSON] et al. [31] studied an insulator defect recognition based on global detection and local segmentation. [PERSON] et al.