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Two-stage bridge point cloud segmentation by fusing deep learning and heuristic methods
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DOI：10.1016/j.measurement.2025.117125.png)
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Abstract
En 中文
The point cloud acquired from the data acquisition equipment is not segmented by components, and reverse engineering without component segmentation has limited value. Existing heuristic methods achieve high segmentation accuracy but are computationally intensive. On the other hand, deep learning models are quick but often lack accuracy and depend on limited datasets. To address these issues, this paper introduces a fusion method that utilizes results trained on an easily created synthetic dataset to initially segment the point cloud roughly, aiming for a segmentation accuracy and intersection over union ratio of 80 % and 70 %, respectively. Subsequently, a streamlined heuristic method is applied to comprehensively segment the point cloud. The verification results of the instance indicate that this approach achieves the same high level of accuracy (>= 99 %) as heuristic methods but increases the speed of segmentation by approximately 2.52 times. The method involves using a synthetic dataset, derived from real point cloud data, in conjunction with the fusion method and selecting a segmentation network that is optimized for simple synthetic datasets.
Keywords:
Point clouds
Deep learning
Heuristic method
Bridge component recognition
Secondary segmentation
Journal
IF:
5.6
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2.0W
Citations:
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