@article{DAI2025, 
author = {Zhou DAI and Yan LIU and Xianyin MAO and Tao GUO and Lianggang XU and Guixian CHENG},
title = {Fusion Transformer Model-Based Segmentation Algorithm for Laser Point Cloud of Distribution Lines},
year = {2025},
journal = {Journal of South China University of Technology (Natural Science Edition)},
volume = {53},
number = {5},
pages = {139-146},
keywords = {Transformer model, distribution line channel, feature fusion, laser point cloud, neural network},
url = {https://www.sciopen.com/article/10.12141/j.issn.1000-565X.240542},
doi = {10.12141/j.issn.1000-565X.240542},
abstract = {As laser point cloud models are crucial for distribution line inspection and management, most distribution channels have constructed laser point cloud models at present. With the increase of the number of models, extracting key component locations (e. g., conductors, insulators) becomes vital. In order to enhance the accuracy and efficiency of segmenting key components such as lines, towers and insulators, this paper presents a segmentation algorithm for laser point cloud of distribution lines based on a fusion Transformer model. Given the need for detailed features in the point clouds of distribution lines, a dual-channel parallel feature extraction module is designed to capture high-frequency and low-frequency features. The low-frequency features are processed via average pooling and a fusion Transformer-based extractor, while the high-frequency features are handled through max pooling and a multi-layer perceptron (MLP) module with convolutional layers. The feature vectors from both channels are then fused to improve the ability of detail feature extraction. Additionally, the fused features are fed back into the MLP module for further refinement, achieving precise point cloud target segmentation. Extensive experiments demonstrate the accuracy and effectiveness of the proposed algorithm. It has potential advantages in many aspects, such as improving the inspection accuracy of unmanned aerial vehicles, enhancing the level of automation, improving the robustness, integrating multi-source data and reducing inspection costs.}
}