水泥混凝土路面错台自动检测方法

张娟; 叶莉; 李伟

doi:10.19818/j.cnki.1671-1637.2016.04.003

水泥混凝土路面错台自动检测方法

doi: 10.19818/j.cnki.1671-1637.2016.04.003

张娟^1,,
叶莉¹,
李伟²

1.
中交第一公路勘察设计研究院有限公司高寒高海拔地区道路工程安全与健康国家重点实验室, 陕西西安 710075
2.
长安大学信息工程学院, 陕西西安 710064

基金项目:

国家科技支撑计划项目 2014BAG05B07

陕西省青年科技新星计划项目 2011KJXX-35

详细信息

作者简介:
张娟(1976-), 女, 河南遂平人, 中交第一公路勘察设计研究院有限公司教授级高级工程师, 工学博士, 从事道路检测技术研究

中图分类号: U416.216
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- 被引次数: 0
出版历程
- 收稿日期: 2016-06-21
- 刊出日期: 2016-08-25

Automatic detecting method of cement concrete pavement fault

ZHANG Juan^1
,,
YE Li¹,
LI Wei²

1.
State Key Laboratory of Road Engineering Safety and Health in Cold and High-Altitude Regions, CCCC First Highway Consultants Co., Ltd., Xi'an 710075, Shaanxi, China
2.
School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

More Information

Author Bio:
ZHANG Juan (1976-), female, senior engineer, PhD, +86-29-88853000, zhangjuan0810@163.com

摘要

摘要: 为实现水泥混凝土路面错台自动检测, 分析了主动光学三维形状测量技术, 利用面阵CCD相机作为图像接收装置, 构建了主要包括电源子系统、图像采集子系统、触发子系统与GPS定位子系统的基于线激光扫描的水泥路面错台检测平台。根据水泥混凝土路面三维检测数据的噪声特点来确定噪声滤波阈值, 先进行均方差滤波, 再对处理后的三维数据进行开运算和腐蚀运算, 消除数据中的孤立点。根据滤波之后的三维数据矩阵大小分成均匀的块, 利用块的均值消除斜面, 再依次取消除斜面之后的三维信息数据矩阵的每一行的拐点, 得出拐点矩阵, 从而计算出错台量。为了验证检测方法的可行性和有效性, 在实验室模拟了平行错台、梯形错台和交叉错台, 按错台量的大、中、小分别进行3次检测, 并与水准仪检测结果进行对比。对同一错台, 用水准仪和三维检测方法分别进行10次重复性检测, 分析了检测方法的重现性。试验结果表明: 检测方法测量结果与水准仪检测结果的绝对误差中有96%小于1mm, 水准仪检测结果的标准差约为三维检测方法的7.8倍, 表明三维检测方法具有较好的准确性、稳定性和可重现性, 可用于水泥混凝土路面错台自动检测。
- 路面工程 /
- 水泥混凝土路面 /
- 错台 /
- 三维检测方法 /
- 线激光
Abstract: In order to realize the automatic detection of cement concrete pavement fault, the active optical 3D shape measurement technique was analyzed, area array CCD camera was used to receive the measurement images, the detecting platform of fault was established based on the line laser 3D scanning, and mainly included the power subsystem, the acquisition subsystem of cement road surface image, the external trigger subsystem and the GPS positioning subsystem. The noise filtering threshold was determined according to the noise characteristics of 3Ddetecting data of cement concrete pavement, the open and corrosion operations were implemented for eliminating the isolated points in the data after the mean square deviation filtering. 3D data matrix was divided into homogeneous blocks according to its size, the blocks mean was used to eliminate the incline of data matrix, the inflection point matrix was obtained according to each row inflection point of 3Ddata matrix, and the faulting values were calculated. In order to verify the feasibility and validity of the method, parallel fault, trapezoid fault and cross fault weresimulated and respectively detected three times according to the fault sizes in the laboratory, and the detecting result was compared with the testing result of leveling instrument. The same fault was repeatedly measured 10 times by the leveling instrument and the 3D detecting method to verify the reproducibility of 3D detection method. Experimental result shows that 96% of absolute errors between the measuring values using the 3D detection method and the leveling instrument are less than 1mm, the standard deviation is about 7.8 times larger using the leveling instrument than using the 3Ddetection method, so the 3Ddetection method has higher accuracy, stability and reproducibility and can be used in the automatic detection of concrete joint fault.
- pavement engineering /
- cement concrete pavement /
- fault /
- 3Ddetection method /
- line laser

HTML全文

图 1 三维轮廓

Figure 1. 3Dcontour

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图 2 测量原理

Figure 2. Measuring principle

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图 3 检测系统

Figure 3. Measuring system

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图 4 三维数据采集系统

Figure 4. 3Ddata acquisition system

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图 5 专用电源

Figure 5. Special power

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图 6 激光器

Figure 6. Laser device

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图 7 3D相机

Figure 7. 3Dcamera

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图 8 错台自动识别流程

Figure 8. Process of faulting automatic identification

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图 9 去噪流程

Figure 9. Denoising process

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图 10 均方差滤波流程

Figure 10. Filtering process of mean square variance

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图 11 滤波结果

Figure 11. Filtering results

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图 12 斜面消除流程

Figure 12. Elimination process of inclined plane

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图 13 试验装置

Figure 13. Experimental device

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图 14 数据采集

Figure 14. Data collection

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图 15 错台模型

Figure 15. Fault models

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图 16 错台测点分布

Figure 16. Measurement points distributions of faults

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图 17 绝对误差分布

Figure 17. Absolute errors distributions

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图 18 测量数据分布

Figure 18. Measuring data distributions

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表 1 测量结果

Table 1. Measuring results

表 2 测点1测试数据

Table 2. Measuring data at measuring point 1

表 3 测点2测试数据

Table 3. Measuring data at measuring point 2

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