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基于三维激光扫描和深度学习的预应力波纹管位置检测方法
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A Position Detection Method of Prestressed Ducts Based on 3D Laser Scanning and Deep Learning
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    摘要:

    预应力波纹管的位置对预应力混凝土构件的承载力有重大影响,为提升波纹管位置检测的效率和精度,提出一种基于三维激光扫描和深度学习的波纹管位置检测方法. 首先运用三维激光扫描技术制作包含预应力波纹管的数据集,然后通过高效大规模三维点云语义分割网络(RandLA-Net)得到波纹管点云,并采用包围盒中点法和圆拟合约束法获取波纹管点云切片中心点,最后通过反向传播神经网络(BP网络)拟合得到波纹管中心线. 使用该方法对实际预应力混凝土小箱梁波纹管位置进行检测,结果表明:即便是在小箱梁钢筋骨架和施工胎架等复杂周围环境的干扰下,该方法也能得到完整的预应力波纹管位置,最大检测误差小于±10 mm,满足相关施工规范要求.

    Abstract:

    The position of prestressed ducts has a significant impact on the bearing capacity of prestressed concrete members. A position detection method for ducts based on 3D laser scanning and deep learning is proposed to improve the efficiency and accuracy of ducts position detection. Firstly, a dataset containing prestressed ducts is produced by the 3D laser scanning method. Then, the ducts point cloud is obtained by an efficient large-scale 3D point cloud semantic segmentation network (RandLA-Net). The centroid of the ducts point cloud slice is obtained by using the bracket box midpoint method and circle fitting constraints method. Finally, the centerline of the ducts is fitted by the back-propagation neural network (BP network). The detection results of actual prestressed concrete box girder duct position using this method show that even under the interference of complex surrounding environments such as box girder reinforcing steel skeleton and construction tire frame, this method can obtain the complete prestressed duct position. The maximum detection error is less than ±10 mm, which meets the requirements of relevant construction specifications.

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朱炯亦 ,王奕 ,蒋海里 ,刘攀攀 ,汪德江 ?,杜晓庆 .基于三维激光扫描和深度学习的预应力波纹管位置检测方法[J].湖南大学学报:自然科学版,2025,52(5):209~218

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  • 在线发布日期: 2025-06-09
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