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Open AccessArticle
Bilinear Distance Feature Network for Semantic Segmentation in PowerLine Corridor Point Clouds
by
Yunyi Zhou
Yunyi Zhou 1,
Ziyi Feng
Ziyi Feng 1,*,
Chunling Chen
Chunling Chen 1,* and
Fenghua Yu
Fenghua Yu 1,2,3
1
College of information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
2
National Digital Agriculture Regional Innovation Center (Northeast), Shenyang 110866, China
3
Key Laboratory of Smart Agriculture Technology in Liaoning Province, Shenyang 110866, China
*
Authors to whom correspondence should be addressed.
Sensors 2024, 24(15), 5021; https://doi.org/10.3390/s24155021 (registering DOI)
Submission received: 8 July 2024
/
Revised: 24 July 2024
/
Accepted: 2 August 2024
/
Published: 2 August 2024
Abstract
Semantic segmentation of target objects in power transmission line corridor point cloud scenes is a crucial step in powerline tree barrier detection. The massive quantity, disordered distribution, and non-uniformity of point clouds in power transmission line corridor scenes pose significant challenges for feature extraction. Previous studies have often overlooked the core utilization of spatial information, limiting the network’s ability to understand complex geometric shapes. To overcome this limitation, this paper focuses on enhancing the deep expression of spatial geometric information in segmentation networks and proposes a method called BDF-Net to improve RandLA-Net. For each input 3D point cloud data, BDF-Net first encodes the relative coordinates and relative distance information into spatial geometric feature representations through the Spatial Information Encoding block to capture the local spatial structure of the point cloud data. Subsequently, the Bilinear Pooling block effectively combines the feature information of the point cloud with the spatial geometric representation by leveraging its bilinear interaction capability thus learning more discriminative local feature descriptors. The Global Feature Extraction block captures the global structure information in the point cloud data by using the ratio between the point position and the relative position, so as to enhance the semantic understanding ability of the network. In order to verify the performance of BDF-Net, this paper constructs a dataset, PPCD, for the point cloud scenario of transmission line corridors and conducts detailed experiments on it. The experimental results show that BDF-Net achieves significant performance improvements in various evaluation metrics, specifically achieving an OA of 97.16%, a mIoU of 77.48%, and a mAcc of 87.6%, which are 3.03%, 16.23%, and 18.44% higher than RandLA-Net, respectively. Moreover, comparisons with other state-of-the-art methods also verify the superiority of BDF-Net in point cloud semantic segmentation tasks.
Share and Cite
MDPI and ACS Style
Zhou, Y.; Feng, Z.; Chen, C.; Yu, F.
Bilinear Distance Feature Network for Semantic Segmentation in PowerLine Corridor Point Clouds. Sensors 2024, 24, 5021.
https://doi.org/10.3390/s24155021
AMA Style
Zhou Y, Feng Z, Chen C, Yu F.
Bilinear Distance Feature Network for Semantic Segmentation in PowerLine Corridor Point Clouds. Sensors. 2024; 24(15):5021.
https://doi.org/10.3390/s24155021
Chicago/Turabian Style
Zhou, Yunyi, Ziyi Feng, Chunling Chen, and Fenghua Yu.
2024. "Bilinear Distance Feature Network for Semantic Segmentation in PowerLine Corridor Point Clouds" Sensors 24, no. 15: 5021.
https://doi.org/10.3390/s24155021
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