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- W4387665286 abstract "We present a 3D point cloud semantic segmentation method for power transmission line based on PointNet++. The method aims to identify pylons, ground, vegetation, insulators, earth wires, conductors, bypass jumpers, etc. We created a dataset of 54,733,560 points, collected from real power transmission lines by airborne lidars. We modified the feature extraction network of PointNet++, the radius, and the number of samplings to fit the complicated structures of the power transmission system components. The experimental results showed accuracy improvement in all 8 categories, the highest improvement is with the insulators, by 2.73%, and the overall improvement is by 0.72%, compared to the original model." @default.
- W4387665286 created "2023-10-17" @default.
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- W4387665286 date "2023-10-17" @default.
- W4387665286 modified "2023-10-17" @default.
- W4387665286 title "A semantic segmentation method for power transmission line point clouds based on PointNet++" @default.
- W4387665286 doi "https://doi.org/10.1117/12.3009543" @default.
- W4387665286 hasPublicationYear "2023" @default.
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