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Development of a modeling method for monitoring tunnel deformation based on active panoramic vision technique
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摘要:
针对隧道距离长、运维检测时间跨度长、可检测时间短、形变数据变化小等特点，本文提出了一种基于主动式全景视觉的隧道全断面变形检测方法。首先通过配置在隧道检测装置上的主动式全景视觉传感器对隧道横断面进行全景扫描，获取隧道全景切面扫描图像；接着通过改进的高斯曲线拟合法提取全景切面扫描图像上的亚像素激光中心点，并采用贝塞尔曲线对其进行平滑处理；然后根据全方位视觉传感器的标定结果解析出隧道横断面上内壁的几何信息；再利用隧道横断面上内壁三维点云数据进行三维重建；最后，对重构的隧道模型进行了精度分析。实验研究表明：基于主动式全景视觉的隧道全断面变形的检测方法具有检测速度快、实时性好、数据全面、可视化程度高等优点，能满足对狭长隧道进行快速定性定量分析的需求。
Abstract:Aiming at the characteristics of long-distance tunnel, long maintenance intervals, limited repairing time and small changes in the deformation data, this paper presents a detection method of tunnel deformation based on active omni-directional vision tunnel section deformation. Firstly, the active panoramic vision sensor (ASODVS) is installed at the tunnel and detecting device scans the cross section of tunnel to acquire tunnel section panoramic images. Secondly, the sub-pixel center of the panoramic image is extracted by the improved Gauss curve fitting method, and the smooth processing is performed by using the Bezier curves. Thirdly, the system analyzes the geometry information of the tunnel cross-section of the inner wall through calibration results of omnidirectional vision sensor. The tunnel cross section 3D point cloud data are used for 3D reconstruction. Finally, the precision of the tunnel reconstruction model is analyzed. The experimental results show the method has the advantages of high speed acquisition, real-time, comprehensive data and good visualization. It can meet the needs of the rapid qualitative and quantitative analyses.
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Abstract: The tunnel is a civil engineering structure and the product for the human breaking through the boundaries of the natural environment, to improve the utilization of underground space and the traffic conditions. In 1970, the Organisation for Economic Co-operation and Development held a tunnel meeting that, after synthesizing the various factors, defined the tunnel as follows: "In some applications, under the ground in any way, shape and the size of cross-sectional area is larger than 2 square metres according to regulations".
As tunnel is an important part of the transport facility, it is necessary to inspect and keep the tunnel system in good condition periodically. The main rock mass structure appears varying degrees in different parts of deformation and significantly lower than design life due to the technology limitations and force majeure during the tunnel construction and its operation. The tunnel health problems impact the path transportation security. How to detect the tunnel deformation quickly becomes the urgent problem to be solved. Most of the existing non-machine vision detection methods have the disadvantages of large measurement errors, long time, less-automated and susceptibility to the surrounding environment. There are some shortcomings of machine vision detection method, such as high cost, large number of images acquisition, complicated data processing and poor real-time performance.
Aiming at the above problems and the characteristics of long-distance tunnel, long maintenance intervals, limited repairing time, and small changes in the deformation data, this paper designs and implements the modeling method for monitoring tunnel deformation based on active stereo-omnidirectional vision sensor. Firstly, the active panoramic vision sensor (ASODVS) which is composed of omni-directional vision sensor (ODVS) and circular laser light source is installed at the tunnel detecting device scanning the cross section of tunnel to acquire tunnel section panoramic images. Secondly, the sub-pixel center of the panoramic image is extracted by the improved Gauss curve fitting method, and the smooth processing is performed by using the Bezier curves. Thirdly, the system analyzes the geometry information of the tunnel cross-section of the inner wall through calibration results of omnidirectional vision sensor. The tunnel cross section 3D point cloud data are used for 3D reconstruction. Finally, the precision of the tunnel reconstruction model is analyzed. The experimental results show the method has the advantages of high speed acquisition, real-time, comprehensive data and good visualization. It can meet the needs of the rapid qualitative and quantitative analyses.
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图 4 中值滤波前后的对比. (a)滤波前. (b)滤波后.
Figure 4. The comparison diagram of median filter. (a) Before filtering. (b) After filtering.
图 5 高斯曲线拟合法提取全景激光线效果. (a)全景图. (b)提取激光线.
Figure 5. The results of extracting the panoramic laser line based on the Gaussian curve approximation. (a) Panorama. (b) Laser extraction.
图 6 隧道内壁三维坐标数据获取示意图.
Figure 6. The diagram of acquiring 3D coordinate data on tunnel lining.
图 7 增长基线距前后成像位置的变化. (a)增长基线距之前. (b)增长基线距之后.
Figure 7. The change of imaging position due to increasing the baseline. (a) No growth baseline. (b) Growth baseline.
图 8 基线距、测量距离和测量误差率的关系.
Figure 8. The relationship among the baseline, measuring distance and measuring error rate.
图 10 实验环境和三维重构. (a)实验环境. (b)走廊部分重构.
Figure 10. Experimental environment and the graph of three-dimensional reconstruction. (a) Experimental environment. (b) Corridor partial reconstruction.
图 11 全景激光图和激光提取图(Ⅰ). (a)全景激光图. (b)激光提取图.
Figure 11. Panorama and laser extraction images (Ⅰ). (a) Panorama. (b) Laser extraction.
图 12 实验环境和三维测量图. (a)实验环境. (b)三维测量图.
Figure 12. The experimental environment and the graph of three-dimensional measurement. (a) Experimental environment. (b) The graph of 3D measurement.
图 13 全景激光图和激光提取图(Ⅱ). (a)全景激光图. (b)激光提取图.
Figure 13. Panorama and laser extraction images (Ⅱ). (a) Panorama. (b) Laser extraction.
图 15 三维重构图(Ⅱ). (a)正面. (b)旋转一定角度.
Figure 15. The graph of three-dimensional reconstruction (Ⅱ). (a) The front. (b) After rotation.
图 16 单截面误差分析. (a)单截面重构. (b)对比图.
Figure 16. The error analysis diagram of cross section. (a) Single section reconstruction. (b) Comparison chart.
表 1 ODVS的标定参数.
Table 1. The calibration parameters of ODVS.
a0 a2 a4 平均误差/ pixel -75.8903 0.0026 0.00001 0.38 A t 中心点 $\left[{\begin{array}{*{20}{c}} {0.999}&0\\ {\begin{array}{*{20}{l}} 0 \end{array}}&1 \end{array}} \right]$ $\left[{\begin{array}{*{20}{c}} {49.461}\\ {-15.692} \end{array}} \right]$ $\left[\begin{array}{l} 311.850\\ 235.891 \end{array} \right]$ 
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表 2 高度和宽度分析表.
Table 2. Height and width analyses.
测量值/mm 实际值/mm 相对误差/(%) 高度 2835.2 2860 0.86 2872.9 2860 0.45 2867.4 2860 0.25 2915.0 2860 1.92 2907.6 2860 1.66 2873.6 2860 0.48 宽度 2648.5 2640 0.32 2735.6 2640 3.62 2728.3 2640 3.34 2705.2 2640 2.47 2712.7 2640 2.75 2605.0 2640 1.33
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表 3 截面面积分析表.
Table 3. Sectional area analyses.
面积 测量值/m3 实际值/m3 相对误差/(%) 不含门
包含门7.7412
8.33957.5504
8.33840.25
0.12
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表 4 特征点位置分析表.
Table 4. The location of feature point analyses.
特征点 测量值/mm 实际值/mm 误差/(%) A (-1289, 1446) (-1320, 1430) 0.46 B (1306, 1453) (1320, 1430) 0.39 C (-1309, -1405) (-1320, -1430) 1.33 D (1358, -1423) (1320, -1430) 1.07
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