基于点云数据的预制叠合板尺寸质量智能检测方法OACSTPCD
Automated dimensional quality assessment of precast laminated panels based on 3D laser scanning
为保证预制叠合板在施工现场能顺利安装,在出厂前通常需要对其进行尺寸质量检测,现有预制叠合板的尺寸质量检测方法难以全面准确地测量叠合板的实际三维尺寸.提出一种基于预制叠合板点云数据的多尺寸质量智能检测方法.对采集的点云数据进行预处理后,利用机器学习算法完成预制叠合板点云的自动定位与分割;依据不同的检测任务,将目标点云沿不同方向降维,映射为二维灰度图像;利用图像特征检测算法,分别实现叠合板底板的长宽、预留胡子筋的出筋长度与间距及桁架钢筋高度的自动检测;在验证试验中,对3块预制叠合板的点云数据进行尺寸质量检测.结果表明,提出的智能检测方法能全面准确地完成预制叠合板出厂尺寸质量检测,能进一步提高预制叠合板非接触质量检测结果的科学性与精准性.
Dimensional quality assessment(DQA)of precast laminated panels(PLPs)is required to ensure the installation of the components on the construction site.However,existing methods cannot meet the precision and comprehensiveness of PLPs'DQA.In this paper,we propose an automated multi-dimensional quality assessment method for PLPs based on point cloud data(PCD).After pre-processing the original PCD,the scanned PLPs'PCD is automatically extracted by the machine learning algorithms,and down-mapped in different directions to generate two-dimensional(2D)images.DQA of PLPs can be realized automatically by the image feature detection algorithms.The validation experiment is conducted on three PLPs.The result shows that the proposed method meets the dimensional quality assessment accuracy of precast laminated panels.
马晓晓;张胜;程国忠;傅丽华;胡申林;李阳
重庆大学山地城镇建设与新技术教育部重点实验室||土木工程学院,重庆 400045成都建工第一建筑工程有限公司,成都 610017
土木建筑
点云数据预制叠合板尺寸质量智能检测三维激光扫描
point cloud dataprecast laminated panelsdimensional quality assessmentautomated assessment3D laser scanning
《土木与环境工程学报（中英文）》 2024 (001)
102-109 / 8
国家重点研发计划(2021YFF0500903);国家自然科学基金(52130801)National Key Research and Development Program of China(No.2021YFF0500903);National Natural Science Foundation of China(No.52130801)
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