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An improved 3D point cloud instance segmentation method for overhead catenary height detection
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DOI：10.1016/j.compeleceng.2022.107685.png)
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Abstract
En 中文
The function of a subway-tunnel catenary is to ensure the safe operation of a subway under highspeed conditions; hence, it plays an irreplaceable role in the subway system. However, owing to bad tunnel environments and repeated vibrations, catenaries can easily become deformed. To solve the above problems, this study proposes a new detection method, which applies the 3D-BoNet instance-segmentation model with a multi-scale grouping (MSG) structure and transfer learning to a 3D point-cloud tunnel dataset. Experiments show that the improved model can effectively segment the left and right tracks and the catenary of a tunnel. A comparative experiment shows that the proposed method improves the average accuracy (mPrec) by 3.8% under the classical index with an intersection-over-union (IoU) threshold of 0.5. Moreover, the computational efficiency is improved by 33.01%. This method has broad application prospects in the research field of 3D point-cloud instance segmentation.
Keywords:
Transfer learning
Point cloud
Curvature downsampling
Instance segmentation
Tunnel catenary conduction height
Random sampling consistency
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