## 1 Introduction  
With the increasing demand and dependence on electricity in modern society, the safe and stable operation of transmission lines, as important conduits for power transmission, has become particularly crucial [1]. However, a large number of insulators inevitably pass through regions with complex environments such as high altitudes and recurring icing. Moreover, being exposed in the wild year-round, combined with the deteriorating climate conditions in recent years, there are frequent occurrences of haze, rainy weather, and frozen snow, making the weather and geographical conditions affecting the insulators increasingly complex. This complexity easily leads to incidents of icing, damage, flashover, etc., posing severe safety risks to the operation of the power system and causing significant economic losses to society [2]. In view of this, to promptly detect and resolve issues with insulators and improve the safety and reliability of transmission lines, insulator monitoring technology has gradually become one of the focal points in the electricity sector. By monitoring the status and performance of insulators in real time, abnormal conditions can be detected in a timely manner, allowing for appropriate maintenance and replacement measures to ensure the safe operation of transmission lines [3]. Currently, insulator defect detection methods include various technical means such as ultrasonic, infrared thermography, vibration, partial discharge, and visual inspection. Ultrasonic detection requires specialized equipment and personnel, making it costly; infrared thermography is significantly influenced by environmental factors and is insensitive to certain defects, making it unable to directly detect internal problems of insulators; vibration detection is heavily affected by external interference, resulting in limited accuracy; and partial discharge detection requires complex equipment and skilled personnel, which also incurs high costs [4]. In contrast, visual inspection is simple and intuitive, allowing for the quick identification of obvious surface defects in insulators. Current visual insulator defect detection methods can be mainly categorized into two types: those based on machine learning image processing algorithms and those based on deep learning algorithms [5]. Machine learning image processing algorithms typically involve feature extraction and classification of images. First, image data are collected from the surface of the insulator, followed by the use of techniques such as edge detection, color analysis, and shape matching to extract feature information from the images. Finally, extracted features are classified and identified using methods like support vector machines and random forests to determine whether defects exist in the insulator. On the other hand, deep learning algorithms utilize deep neural networks for end-to-end learning and processing of raw images. By constructing models such as deep convolutional neural networks (CNNs) and recurrent neural networks (RNNs), features and patterns are learned directly from image data, enabling automatic detection and identification of the insulator [6]. Reference [7] improved the YOLOv7 model by incorporating a coordinate attention mechanism into the multi-scale feature fusion process while also designing a C3 GhostNetV2 module to capture long-range dependencies between different spatial pixels and reduce the model's parameters and floating-point operations. However, this algorithm does not account for the insufficient capture of local details caused by small receptive fields, which might lead to the neglect of global context information. Reference [8] proposed a defect detection algorithm called FasterNet-YOLOv5, which combines FasterNet-tiny and the YOLOv5 model.