摘要
随着双碳目标的提出，国家鼓励建筑行业向绿色转型，推动装配式建筑的发展。三维激光雷达等三维点云技术的应用逐渐增多，因其无接触、高精度的特点，在建筑构件检测中发挥重要作用。本文系统探讨了三维点云技术在建筑构件检测中的应用，包括点云的特征、数据处理方法及深度学习技术的结合，点云数据具有数据量大、离散性、非规则性和光学特征等特性，这些特征为构件尺寸测量提供了基础。通过点云匹配法、特征提取、分割技术和深度学习方法，可以显著提高检测的准确性和效率。本文旨在为建筑行业相关从业人员提供一些建议。
关键词： 激光点云；建筑预制构件检测；深度学习；双碳目标
Abstract
With the proposal of the dual carbon goal, the state encourages the construction industry to transform to green and promotes the development of prefabricated buildings. The application of 3D point cloud technology such as 3D lidar is gradually increasing, and it plays an important role in the detection of building components because of its non-contact and high-precision characteristics. This paper systematically discusses the application of 3D point cloud technology in the detection of building components, including the combination of point cloud characteristics, data processing methods and deep learning technology. Through point cloud matching method, feature extraction, segmentation technology and deep learning methods, the accuracy and efficiency of detection can be significantly improved. The purpose of this article is to provide some advice for those involved in the construction industry.
Key words： Laser point clouds; Inspection of prefabricated building components; Deep learning; Carbon peaking and carbon neutrality goals
参考文献 References
[1] https://xueqiu.com/2868304221/29603021.
[2] 吴一全,陈慧娴,张耀.基于深度学习的三维点云处理方法研究进展[J].中国激光,2024,51(05):143-165.
[3] 李东声.基于点云数据的建筑构部件尺寸质量智能检测算法研究[D].重庆大学,2020.
[4] 张伟、李明、王强.三维点云数据处理在建筑预制构件尺寸检测中的应用[J].计算机辅助设计与图形学学报, 2021,.33(9).
[5] Xiaoyu Wang,Yongxiang Li,Liansheng Tan.Deep Learning for Point Cloud-Based 3D Object Detection: A Survey[J]. IEEE Transactions on Automation Science and Engineering, 2023,30(2).
[6] 赵敏,刘洋,孙丽.基于改进点云匹配算法的建筑预制构件尺寸检测方法研究[J].中国机械工程,2022,33(12).
[7] Kim M-K , Sohn H , Chang C-C . Automated dimensional quality assessment of precast concrete panels using terrestrial laser scanning[J]. Automation in Construction, 2014, 45:163-177.
[8] Kim M-K, Wang Q, Park J-W, Cheng J.C.P., Sohn H, Chang C-C, Automated dimensional quality assurance of full-scale precast concrete elements using laser scanning and BIM, Automation in Construction, 2016, 72(2):102–114.
[9] Zhao W, Jiang Y, Liu Y, Shu J. Automated recognition and measurement based on three-dimensional point clouds to connect precast concrete components[J]. Automation in Construction, 2022,133:104000.
[10] Wang C , Cho Y K , Kim C . Automatic BIM component extraction from point clouds of existing buildings for sustainability applications[J]. Automation in Construction, 2015, 56:1-13.
[11] Bosche F, Guenet E, Automating surface flatness control using terrestrial laser scanning and building information models[J]. Automation in Construction, 2014, 44 :212–226.
[12] 尚栋．基于三维激光扫描的桥梁几何偏差检测方法研究[D].重庆:重庆交通大学,2021.
[13] Alzraiee H , Sprotte R , Ruiz A L . Quality Control for Concrete Steel Embed Plates using LiDAR and Point Cloud Mapping. 37th International Symposium on Automation and Robotics in Construction (ISARC 2020).
[14] 李华,王强,赵敏.基于改进特征提取方法的三维点云匹配算法研究[J].软件学报,2020,61(3).
[15] 王新雅．基于三维激光点云的预制混凝土箱梁尺寸检测和BIM逆向建模[D].南京:东南大学,2022.
[16] 巩育江．基于几何特征的点云分割算法研究进展[J].激光技术,2022,46(.3).:.326-334.
[17] 张伟,李明,王强.基于深度学习的三维点云匹配算法及其在建筑预制构件检测中的应用[J].计算机工程与应用,2021,57(15).
[18] 王强,李杰,赵敏.基于深度学习的三维点云匹配算法研究[J].模式识别与人工智能,.2019,32(6).