## 1 Introduction  
In winter, transmission lines are affected by severe weather such as rain and snow. This can cause ice to form on them, which may lead to tower collapse and disrupt the normal operation of the power system [1, 2]. Since most transmission lines are built in remote, wild environments, power operation and maintenance personnel are unable to understand the ice status in real time, which brings great inconvenience to deicing work. Deicing robots on transmission lines can perform automated deicing [3, 4]. The robot's camera captures images of ice-covered transmission lines and uses a deep learning algorithm to calculate the thickness of the ice, thereby controlling the robot to perform resonant deicing [5]. The calculation of ice thickness is key to the entire deicing process. Since the network coverage in the field environment where the transmission lines are installed is poor, cloud data transmission and calculation cannot be achieved. Therefore, it is necessary to identify the ice thickness on the robot's side [6].  
Among the methods of ice-thickness recognition, the use of image detection technology is currently one of the mainstream methods [7]. The principle is to segment the ice-covered area from the non-ice-covered area in the ice-covered image through a deep learning algorithm and then count the pixels in the ice-covered area to calculate the ice thickness [8]. Image segmentation is a computationally intensive computer vision task [9], which requires more computing resources than image classification [10] and object detection algorithms [11]. Currently, commonly used image segmentation algorithms such as U-Net [12] and Deeplab-V3 [13] have complex network models, while the computing power of edge devices is limited. Such algorithms are not suitable for computing on edge devices [14]. Therefore, how to design an ice-thickness detection algorithm suitable for edge devices is the focus of this article.  
We propose a lightweight transmission line ice-thickness recognition network based on efficient dynamic perception. The main framework of EDPNet is the encoding and decoding architecture, which has been redesigned into the following three parts: the encoding part, decoding part, and loss function. The LMRC block is designed in the encoding part. By splitting the ordinary convolution and combining separable asymmetric convolutions, the number of parameters is greatly reduced without compromising the feature extraction capability. In the decoding part, we propose LDFM to dynamically fuse the encoding and decoding features through a lightweight attention module. Finally, in the loss function part, a dynamic loss function is designed to train the model in stages. After the model optimization becomes stable, we introduce boundary refinement loss for more accurate model training. Therefore, our contributions can be summarized as follows:  
1. A lightweight multidimensional recombination convolution (LMRC) block is designed to efficiently extract ice image features.
2. A lightweight deep fusion module (LDFM) is proposed to perform lightweight dynamic fusion of encoding and decoding features.
3. A new dynamic loss function is designed, and a boundary refinement loss function is introduced to refine model training.
4. It was trained on a dataset of ice-covered transmission lines and deployed on an OrangePi5 Plus edge device, with a thickness recognition error of 4.2% and a detection speed of 10 FPS.  
The structure of this paper is as follows: Chapter 1 introduces the research background and significance of identifying the thickness of ice cover on transmission lines. Chapter 2 introduces related work. Chapter 3 elaborates on the composition of EDPNet. Chapter 4 presents experimental results and analysis. And finally, the conclusion is drawn.