# Ice Thickness Detection of Transmission Lines Based on Cross-Guide-UNet
## 1 Introduction  
Nowadays, electricity plays an important role in the daily routines of individuals, underlining the importance of a reliable power supply system. Overhead transmission lines serve as the primary conduit for electricity transmission, making the stability of these lines pivotal for the uninterrupted functioning of the power grid. However, owing to their elevated positioning, transmission lines are highly vulnerable to adverse weather conditions and environmental factors, including strong winds and thunderstorms, as well as the accumulation of ice and snow [1, 2]. Ice buildup on transmission lines represents a common fault type that disrupts the operation and maintenance of power systems [3, 4]. Severe ice cover can even cause transmission towers to collapse, disrupting people's daily lives [5, 6]. In order to effectively solve the problem of ice on transmission lines and ensure the stable operation of the power system, our team developed an intelligent de-icing robot,which is equipped with a camera module, vibration module, main control module, and AI module. This robot can accurately measure the ice layer thickness on iced transmission lines in real time and precisely calculate the vibration intensity required for efficient de-icing based on the thickness. Ice layers of different thicknesses have distinct characteristics and require significantly different vibration intensities. Thin ice layers are fragile, and excessive vibration intensity will waste energy and damage the lines, while thick ice layers need sufficient vibration to be removed. Our robot can dynamically adjust the vibration intensity to precisely adapt to various ice layers, minimizing the impact on the lines while de-icing and strongly ensuring the normal operation of the power system. The detection of ice thickness on transmission lines primarily involves two approaches: mechanical modeling and image detection. Mechanical modeling entails calculating ice thickness by constructing a physical model [7]. [PERSON] et al. [8] achieved good results in artificial icing experiments by constructing a cylindrical array-based ice monitoring device and using a Makkonen model and related algorithms to measure ice weight and invert environmental parameters to detect ice thickness. [PERSON] et al. [9] established a state equation for transmission lines and used an iterative method based on axial tension measurement to detect the equivalent ice thickness of 500 kV overhead lines. Their accuracy was verified through practical cases and finite element simulations. However, this method is susceptible to wind load interference and relies on specific line parameters for measurement accuracy, which limits its applicability in difficult parameter acquisition scenarios. However, this study has shortcomings in terms of adaptability to complex environments and measurement accuracy of some parameters. When calculating the thickness of ice layers through mechanical modeling, environmental factors are usually ignored [10]. This will cause a deviation between the actual ice and the model calculation results. For example, in low-temperature, high-humidity, and light-wind environments, micro vibrations of the circuit affect the condensation and growth of ice layers, and mechanical modeling does not consider these changes, which cannot provide accurate operational basis for de-icing robots. Image detection has emerged as a popular method, wherein images of ice-covered transmission lines are captured using image acquisition, devices and ice thickness is subsequently determined through image algorithms [11]. Image detection offers high safety and reliability compared with alternative methods [12]. At present, most detection methods use semantic segmentation technology, which divides the foreground and background through computer vision technology [13]. The Sharp U-Net [14] architecture was used to design deep convolution and spatial filters specifically for encoders to improve segmentation performance. [PERSON] et al.