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.
[1] |
中华人民共和国住房和城乡建设部. 建筑工程施工质量验收统一标准: GB 50300—2013[S]. 北京: 中国建筑工业出版社, 2014. |
[2] |
赵馨怡, 郭晓. 基于BIM和三维激光扫描技术的建设工程质量检测方法研究[J]. 土木建筑工程信息技术, 2020, 12(05): 131-134. |
[3] |
吴国强, 俞家勇, 马巍, 等. 基于三维激光扫描的施工建筑平整度检测方法[J/OL]. 激光与光电子学进展: 1-13[2023-08-08]. |
[4] |
杜伸云, 胡伟. 基于三维激光扫描的桥梁钢结构构件质量检查方法研究[J]. 土木建筑工程信息技术, 2017, 9(05): 113-117. |
[5] |
陈滨津, 姚守俨, 蒋绮琛, 等. BIM+三维激光扫描技术在工程质量管控中的应用[J]. 土木建筑工程信息技术, 2019, 11(05): 55-60. |
[6] |
周中, 闫龙宾, 张俊杰, 等. 基于深度学习的公路隧道表观病害智能识别研究现状与展望[J]. 土木工程学报, 2022, 55(S2): 38-48. |
[7] |
钟新谷, 彭雄, 沈明燕. 基于无人飞机成像的桥梁裂缝宽度识别可行性研究[J]. 土木工程学报, 2019, 52(04): 52-61. |
[8] |
覃亚伟, 石文洁, 肖明钊. 基于BIM+三维激光扫描技术的桥梁钢构件工程质量管控[J]. 土木工程与管理学报, 2019, 36(04): 119-125.doi: 10.3969/j.issn.2095-0985.2019.04.019 |
[9] |
杜理强, 邹海涛, 童宇超, 等. 杭州西站大跨屋盖钢结构三维激光扫描变形监测技术[J]. 土木建筑工程信息技术, 2022, 14(04): 41-47. |
[10] |
刘界鹏, 崔娜, 周绪红, 等. 基于三维激光扫描的房屋尺寸质量智能化检测方法[J]. 建筑科学与工程学报, 2022, 39(04): 71-80+3-4. |
[11] |
叶冬明, 李武, 谢忠, 等. 预制混凝土剪力墙施工安装偏差实测及结构安全性影响分析[J]. 建筑结构, 2022, 52(S2): 2833-2839. |
[12] |
苍桂华, 岳建平. 基于加权总体最小二乘法的点云平面拟合[J]. 激光技术, 2014, 38(03): 307-310. |
[13] |
Justo A, Soilán M, Sánchez-Rodríguez A, et al. Scan-to-BIM for the infrastructure domain: Generation of IFC-compliant models of road infrastructure assets and semantics using 3D point cloud data[J]. Automation in Construction, 2021, 127: 103703.doi: 10.1016/j.autcon.2021.103703 |
[14] |
Guo J, Wang Q, Park J H. Geometric quality inspection of prefabricated MEP modules with 3D laser scanning[J]. Automation in Construction, 2020, 111: 103053.doi: 10.1016/j.autcon.2019.103053 |
[15] |
Duan R, Deng H, Tian M, et al. SODA: A large-scale open site object detection dataset for deep learning in construction[J]. Automation in Construction, 2022, 142: 104499.doi: 10.1016/j.autcon.2022.104499 |
[16] |
Hu D, Gan V J L, Yin C. Robot-assisted mobile scanning for automated 3D reconstruction and point cloud semantic segmentation of building interiors[J]. Automation in Construction, 2023, 152: 104949.doi: 10.1016/j.autcon.2023.104949 |
[17] |
Xiong X, Adan A, Akinci B, et al. Automatic creation of semantically rich 3D building models from laser scanner data[J]. Automation in construction, 2013, 31: 325-337.doi: 10.1016/j.autcon.2012.10.006 |
[18] |
毋雪雁, 王水花, 张煜东. K最近邻算法理论与应用综述[J]. 计算机工程与应用, 2017, 53(21): 1-7.doi: 10.3778/j.issn.1002-8331.1707-0202 |
[19] |
Schult J, Engelmann F, Hermans A, et al. Mask3D: Mask Transformer for 3D Semantic Instance Segmentation[C]//2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023: 8216-8223. |
计量
- PDF下载量(7)
- 文章访问量(351)
- HTML全文浏览量(195)