## 2 Related Work  
Currently, there are three types of algorithms for identifying the ice thickness of a transmission line: manual measurement method [15], physical model method [16], and image detection method [17]. The manual measurement method involves the power grid staff climbing up the transmission tower to measure the ice thickness of the transmission line. This method has the most accurate measurement results, but it is dangerous. Moreover, the manual measurement method cannot meet the needs of real-time detection. Usually, the measurement and deicing operation can only be carried out after the transmission line is covered with ice. Therefore, this method is not widely used at present. The physical model method collects temperature, humidity, and meteorological data through sensors, then builds an ice-thickness prediction model and sends the collected data into the model for prediction. [PERSON] et al. [18] studied and investigated the mechanism of icing on transmission lines and summarized the environmental factors under different icing conditions. [PERSON] et al. [19] constructed an ice-covered transmission line prediction model based on meteorological characteristics and achieved good results in experiments on different datasets. This method reduces the danger of on-site operations, but it needs to consider many influencing factors and relies heavily on expert knowledge and experience, so the prediction results are often not accurate enough. Moreover, when the input variables change, the prediction model also needs to be redesigned [20]. The most popular method for identifying the ice thickness on transmission lines is the image detection method, which uses a camera to obtain an image of the ice-covered transmission lines and then uses an image algorithm to calculate the ice thickness [21]. The image detection method can obtain the ice thickness on transmission lines in real time and intuitively. Image detection methods can be divided into traditional edge detection methods [22] and image segmentation methods. Traditional edge detection methods use edge algorithms to extract the ice edge of a transmission line, then calculate the pixel difference between the upper and lower edges and obtain the ice thickness by comparing the pixel difference with the normal transmission line. [PERSON] et al. [23] used the Ratio edge detection operator to perform edge detection on an ice-covered transmission line image and then calculated the ice thickness. [PERSON] et al. [24] used morphological processing methods to preprocess the ice-covered image, then used the Line Segment Detector (LSD) algorithm for edge detection, and finally compared it with the diameter of the transmission line to calculate the ice thickness. The limitation of this method is that irregular ice will form on the surface of the transmission line, and the pixel difference between the upper and lower edges is obvious, which is easily affected by errors. Secondly, most current studies use traditional edge detection operators such as Canny [25] and Sobel [26]. These operators cannot accurately identify the ice edge when faced with complex scenes, resulting in the inability to detect the ice thickness. The image segmentation method distinguishes the ice-covered area from non-ice-covered area and then counts the number of pixels in the ice-covered area and compares it with the number of pixels in the non-ice-covered area to obtain the ice thickness on transmission lines. This type of method often uses deep learning technology to segment ice-covered areas, such as U-Net. [PERSON] et al. [27] combined U-Net with a generative adversarial network and used Swin Transformer and Convolutional Block Attention Module (CBAM) to perform image segmentation of ice-covered transmission lines. [PERSON] et al. [28] denoised the ice-covered transmission line image, then segmented the ice-covered area and calculated the ice thickness using regional pixel statistics. [PERSON] et al.