2024, 16(3): 13-18. doi: 10.16670/j.cnki.cn11-5823/tu.2024.03.03
基于激光点云语义分割的工程尺寸质量检测方法研究
| 1. | 中建三局第一建设工程有限责任公司，武汉 430040 |
| 2. | 华中科技大学 土木与水利工程学院，武汉 430074 |
Research on Engineering Dimensional Quality Detection Method Based on Laser Point Cloud Semantic Segmentation
| 1. | The First Construction Co., Ltd., of China Construction Third Engineering Bureau, Wuhan 430040, China |
| 2. | Huazhong University of Science and Technology, School of Civil and Hydraulic Engineering, Wuhan 430074, China |
引用本文:
周炜, 李磊, 詹健江, 张爽, 文江涛. 基于激光点云语义分割的工程尺寸质量检测方法研究[J]. 土木建筑工程信息技术,
2024, 16(3): 13-18.
doi: 10.16670/j.cnki.cn11-5823/tu.2024.03.03
Citation:
Wei Zhou, Lei Li, Jianjiang Zhan, Shuang Zhang, Jiangtao Wen. Research on Engineering Dimensional Quality Detection Method Based on Laser Point Cloud Semantic Segmentation[J]. Journal of Information Technologyin Civil Engineering and Architecture,
2024, 16(3): 13-18.
doi: 10.16670/j.cnki.cn11-5823/tu.2024.03.03
摘要:利用三维激光扫描技术辅助房屋尺寸质量检测，可解决传统人工测量方法人力消耗大、检测率低的问题，但采集到的点云数据通常需要手工分割以提取检测平面，效率低下。因此，本研究提出一种基于点云语义分割的建筑尺寸质量检测方法，包括点云数据轻量化、智能平面分割、智能语义识别和尺寸质量检测四个方面。该方法有效实现了房屋点云数据的自动分割，能基于三维点云数据对室内顶底板标高差、门窗洞口尺寸等指标进行自动测算，预期能提高施工阶段质量验收工作的效率以及测量的准确性。
Abstract: 3D laser scanning technology is applied to assist the dimensional quality inspection of houses, which can solve the problems of high labor consumption and low detection rate of traditional manual measurement methods. However, the collected point cloud data usually need to be manually segmented to extract the inspection plane, which is inefficient. Therefore, this paper proposes an engineering dimensional quality inspection method based on semantic segmentation of laser point cloud, including four aspects: point cloud data simplification, intelligent plane segmentation, intelligent semantic recognition, and dimensional quality inspection. This method effectively realizes the automatic segmentation of the point cloud data of the houses, and can automatically measure the indoor top and bottom plate elevation difference, door and window opening dimensions and other indexes based on the 3D point cloud data, which is expected to improve the efficiency and accuracy of the acceptance work of the quality of construction projects.
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