## 5 Conclusions  
Aiming at the task of recognizing the ice thickness on transmission lines on the end-side equipment, this paper proposes EDPNet, a network for recognizing ice thickness  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance Parameter** & **Before Quantification** & **After Quantification** \\ \hline mIoU/\% & 93.4 & 82.9 \\ F1-Score/\% & 96.5 & 88.4 \\ Time/ms & 96 & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative experimental results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**CPU** & **Frequency** & **Consumption/w** & **Time/ms** \\ \hline \multirow{4}{*}{RK3588} & 2.3 GHz & 4.25 & 91 \\  & 1.8 GHz & 3.92 & 93 \\ \cline{1-1}  & 1.0 GHz & 3.20 & 96 \\ \cline{1-1}  & 0.4 GHz & 2.75 & 101 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Energy consumption experiment results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Performance Parameter** & **Before Quantification** & **After Quantification** \\ \hline mIoU/\% & 93.4 & 82.9 \\ F1-Score/\% & 96.5 & 88.4 \\ Time/ms & 96 & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative experimental results.  
on transmission lines based on efficient dynamic perception. The LMRC lightweight convolution block is first proposed to accelerate the encoder feature extraction speed. Then, a lightweight deep fusion module is added to the decoder to improve the accuracy of ice segmentation of transmission lines with minimal parameters. Then, a dynamic loss function is proposed, and in the second half of model training, the output of ice segmentation images is optimized using the boundary refinement loss function. Experiments were conducted on the transmission line ice dataset, and the segmentation accuracy mIoU reached 93.4% and the F1-Score was 96.5%. The model was deployed on OrangePi5 Plus; the number of model parameters was reduced by six times. The experimental results show that the EDPNet proposed in this paper can complete the task of identifying the ice thickness on transmission lines on edge devices. This is of great significance for the automation operation and maintenance of the power system, improving the efficiency of monitoring the status of transmission lines and preventing the harm caused by ice cover on transmission lines.  
Since image segmentation is a computationally intensive task in computer vision, its recognition speed on edge devices is not fast enough. Due to extreme weather conditions, it is difficult to obtain the thickness of ice cover using visual methods, which can lead to image blurring and inaccurate recognition. Therefore, the next step is to reduce the number of parameters and computational complexity of the model through pruning, optimizing network models, and knowledge distillation techniques, thereby reducing the model's demand for computing resources and improving detection speed on edge devices. On the other hand, attempts are being made to explore multimodal learning that combines visual and other meteorological data to provide more accurate recognition of ice cover thickness.  
Conceptualization, Y.J. and Y.D.; methodology, Y.J. and L.Z.; software Y.J.; validation, Y.D. and Y.Z.; formal analysis, Y.J., Y.Z. and Q.L.; resources, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Shanxi Provincial Higher Education Science and Technology Innovation Project (Grant number 2022L524) and the Shanxi Provincial Key Research and Development Project (Grant number 202102060301020).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.