# Ice Thickness Detection of Transmission Lines Based on Cross-Guide-UNet
## 2 Related Works  
Edge detection, a fundamental task in computer vision, has evolved over 40 years, giving rise to numerous classic recognition algorithms. Traditional methods for edge detection involve manual rule design based on features such as texture [19]. Initially, researchers computed the image gradient to extract edge information using methods such as Sobel [20], Robert [21], Laplacian [22], and the Canny operator [23]. Subsequently, learning-based approaches integrated these low-level features for enhanced edge detection. In recent years, deep learning has undergone substantial advancements, leading to the development of popular edge detection algorithms based on convolutional neural networks. Among these, HED [24] is the first to propose an end-to-end edge detection algorithm that aggregates feature maps from each branch to generate the final output, accompanied by a weighted cross-entropy loss function. RCF [25] further enhances feature fusion across convolutional layers to capture image information at multiple scales. LPCB [26] introduces an end-to-end network structure utilizing VGG as the backbone, integrating the dice loss function [27] and cross-entropy loss function. BDCN [28] introduces a bidirectional cascade network structure, wherein labeled edges supervise each layer at a specific scale; moreover, it introduces a scale enhancement module for multi-scale feature generation. DexiNed [29] presents a robust edge detection system combining HED with the Xception network [30]. PiDiNet [31] introduces a new pixel difference convolution, which integrates traditional edge detection operators into the convolution operation. UAED [32] exploits uncertainty to examine the subjectivity and ambiguity of different annotations in edge detection datasets, transforming the deterministic label space into a learnable Gaussian distribution whose variance measures the ambiguity between different annotations. Edge detection algorithms can be used to detect the edges of ice-covered transmission lines. [PERSON] et al. [33] used the Canny edge detection operator used for the aforementioned purpose, but its detection accuracy was not high. [PERSON] et al. [34] used the ratio operator on ice cover infrared images to detect edges and calculate ice thickness. However, the ratio operator is sensitive to noise, which limits its practical application. [PERSON] et al. [35] used a hybrid approach combining Gaussian and bilateral filters with the Canny operator for edge detection of ice-covered transmission lines, effectively improving detection accuracy, but the calculation process of ice thickness became more complex. [PERSON] et al. [36] introduced a linear detection algorithm based on the LSD algorithm to detect ice thickness on transmission lines, but the detection effect is poor in complex backgrounds. Due to the limitations of manually designing features using traditional methods, the edge detection accuracy of these methods is not high, and they are highly sensitive to noise, resulting in poor performance in practical applications. [PERSON] et al. [37] proposed a robust generalized convolutional neural network for thickness detection of ice-covered transmission lines. They introduced the IBP algorithm to determine the number of convolutional layers and kernels. But its model training is relatively complex and requires the optimization of many parameters. [PERSON] et al. [38] segmented the ice-covered area and the background area and trained a model for ice thickness detection using transfer learning with an attention mechanism. But it is not accurate enough in processing edge details, resulting in difficulty in feature recognition at the edges. [PERSON] et al. [39] used image enhancement techniques to expand the dataset of ice-covered transmission lines and then directly identified the ice thickness of transmission lines using Mask RCNN network. However, due to the limitations of the object detection method used, it can only identify the ice thickness in the interval and cannot accurately identify the ice thickness.