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Computer vision application programming for settlement monitoring in a drainage tunnel

机译:排水隧道沉降监测的计算机视觉应用程序设计

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The study employs computer vision technology to pose a new type of optical instrument composited of the Raspberry Pi, digital cameras and chessboards for structural settlement monitoring. Using the OpenCV functions findChessboardCorners and cornerSubPix, displacement measurements in five distances between a camera and chessboard at 15, 17, 20, 23 and 25 m were undertaken to analyze measuring standard deviations that were from 0.027 to 0.048 pixels in laboratory tests. For field testing, five optical settlement instruments were installed at a drainage tunnel that was constructed at an 80-m-depth location within a landslide area. The resolution and accuracy of the instrument can be determined at 0.01 and 0.11 cm, respectively, as an optimally installed distance of 20 m in the drainage tunnel. The maximum settlement amount of field monitoring was 0.61 cm in six months. Overall, the optical instrument is more cost-effective and IOT-based for a long-term settlement monitoring.
机译:这项研究利用计算机视觉技术来构成一种新型光学仪器,该光学仪器由Raspberry Pi,数码相机和棋盘组成,用于结构沉降监测。使用OpenCV函数findChessboardCorners和cornerSubPix,在相机和棋盘之间的15、17、20、23和25 m的五个距离中进行位移测量,以分析在实验室测试中测量的标准偏差(从0.027到0.048像素)。为了进行现场测试,在排水管道中安装了五台光学沉降仪,该排水管道建在滑坡区域内80米深的位置。仪器的分辨率和精度可以分别确定为0.01和0.11 cm,这是排水隧道中20 m的最佳安装距离。六个月内,现场监控的最大沉降量为0.61厘米。总体而言,该光学仪器更具成本效益,并且基于物联网,可进行长期沉降监测。

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