# Ice Thickness Detection of Transmission Lines Based on Cross-Guide-UNet
## 1 Introduction
[15] conducted research on short-range context modeling and proposed an effective single-stage learning paradigm. [PERSON] et al. [16] employed a fully convolutional neural network for the semantic segmentation of ice cover images, subsequently using the segmented images to calculate ice thickness. [PERSON] et al. [17] developed intelligent ice detection equipment and used the lightweight neural network MobileNetV3 for ice thickness detection. [PERSON] et al. [18] introduced an enhanced multi-threshold algorithm to separate ice-covered pixels and employed morphological enhancement to identify regions of interest. Although these image detection methods use semantic segmentation algorithms to determine ice thickness and detect ice-covered edges, the high accuracy required by de-icing robots with respect to the determination of ice thickness necessitates a different approach. Hence, we used an edge detection algorithm to identify the upper and lower edges of ice-covered transmission lines and subsequently calculate the difference in ice thickness before and after ice coverage to accurately determine ice thickness. Although image detection methods have the advantages of safety and reliability over mechanical modeling methods, existing methods for calculating ice thickness cannot meet the high-precision calculation needs of de-icing robots. When there are obstructions in the surrounding area, the semantic segmentation model is prone to misjudgment, missing the ice layer area and increasing the calculation error of ice thickness. In some practical situations, misjudgment can lead to excessive vibration of the de-icing robot on thin ice layers, damaging the circuit, and insufficient vibration on thick ice layers, resulting in a poor de-icing effect. Therefore, this article proposes a new edge detection algorithm to detect the upper and lower edges of ice-covered transmission lines and then calculate the thickness difference before and after icing to obtain accurate ice thickness. Due to the direct relationship between the thickness of detection and the intensity of vibration, the accuracy requirements for edge detection are more stringent. This study improved the neural network model and loss function to enable the model to more accurately predict the edges of ice-covered transmission lines. The CG-UNet edge detection algorithm proposed in this article adopts the framework of an encoder and a decoder. In response to the problem of insufficient feature fusion between encoding and decoding, a novel cross-guide module is proposed at the crosslayer connection between encoding and decoding. This innovative design significantly improves the guidance effect of the image upsampling restoration process. Secondly, in response to the problem of confusion in ice edge pixel detection, the loss function was improved by designing a joint loss function from both local and global perspectives, effectively enhancing the model's ability to distinguish pixels near the edges. The main contributions of this study can be summarized as follows:  
1. Dataset innovation: We developed an edge detection dataset specifically designed for ice-covered transmission lines. This dataset provides rich samples for model training. 2. Cross-guide module (CGM): In the CG-UNet algorithm, we innovatively introduced the cross-guide module. This module optimizes the upsampling process of images by introducing guidance information in the crosslayer connection between the encoder and decoder, thereby achieving high-precision edge restoration. 3. Joint loss function: We designed a novel joint loss function that combines local and global information, significantly improving the model's ability to discriminate pixels near edges. The design of this loss function makes our model more robust when dealing with easily confused pixels. 4. Edge detection network with encoding and decoding structure: We designed an efficient encoding and decoding structure network for detecting ice thickness.