基于数字图像技术的桥梁裂缝检测综述

doi:10.13229/j.cnki.jdxbgxb.20221475

摘要/Abstract

摘要：

传统的桥梁裂缝检测主要基于人眼识别，检测效率、精度低，而且人眼识别存在受光照影响大、桥塔、高墩等高空位置无法检测及主观性强的问题。近年来，国内外诸多学者为了解决上述问题，研发了许多基于数字图像技术的桥梁裂缝检测设备，像搭载高清相机的桥梁检测车、无人机、爬墩机器人等。同时，拥有高效、高精度的裂缝检测算法更是裂缝检测的基础，如何权衡检测速度与精度一直也是众多学者研究的热点问题之一。本文就近年来国内外基于数字图像技术的桥梁裂缝检测设备、相机的搭载平台与标定方法、预处理算法、传统检测算法、深度学习算法、裂缝特征计算、图像拼接算法以及裂缝的三维输出与监测等方面展开综合评述。此外，对研究过程中存在的不足进行了总结，并从桥梁裂缝检测方法、裂缝三维表达、裂缝的监测跟踪管理和桥梁刚度损失评价及预警等方面进行了展望。

关键词: 桥梁工程, 裂缝识别, 图像处理, 卷积神经网络, 深度学习, 智能检测

Abstract:

As one of the important contents of bridge health detection， crack detection reflects the stress and damage state of bridge structure. The traditional bridge crack detection is mainly based on human eye recognition， of which efficiency and accuracy are both low. Moreover， the human eye recognition has the following problems such as effected greatly by illumination， incapability to detect in some high-altitude positions like bridge towers and high piers and strong subjectivity. In recent years， many scholars at home and abroad have developed many bridges crack detection equipment based on digital image technology to solve the above problems， such as bridge detection vehicles equipped with high-definition cameras， drones， and climbing robots. Meanwhile， the efficient and high-precision crack detection algorithm is the basis of crack detection. How to balance the detection speed and accuracy has always been one of the hot issues studied by many scholars. In this paper， the bridge crack detection equipment based on digital image technology， the platform and calibration method of camera， preprocessing algorithm， traditional detection algorithm， deep learning algorithm， crack feature calculation， image stitching algorithm and three-dimensional output and monitoring of cracks are reviewed. In addition， summaries to deficiencies in the study and prospects the bridge crack detection method， crack three-dimensional expression， crack monitoring and management， bridge stiffness loss evaluation and early warning for the future development trend.

Key words: bridge engineering, crack identification, image processing, convolutional neural network, deep learning, intelligent detection

中图分类号:

U446.3

杨国俊,齐亚辉,石秀名. 基于数字图像技术的桥梁裂缝检测综述[J]. 吉林大学学报(工学版), 2024, 54(2): 313-332.

Guo-jun YANG,Ya-hui QI,Xiu-ming SHI. Review of bridge crack detection based on digital image technology[J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(2): 313-332.

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常见的桥梁裂缝检测去噪算法对比"

名称	描述	优点	缺点
均值滤波^［26］	线性滤波器，用邻域像素的均值代替中心像素值	方法简单，计算速度快，在去除高斯噪声上表现较好	去噪过程中，会造成图像一定程度的细节损坏，使图像变得模糊
中值滤波^［27-32］	非线性滤波器，用邻域像素的中值代替中心像素值，减少图像模糊程度，在此基础上进一步衍生出了自适应中值滤波器等	在去除椒盐噪声、脉冲噪声的同时又能保留图像细节	对结构复杂的图像在去除噪声的过程中，会损失图像的细节，破坏图像的几何结构
高斯滤波	线性滤波器，对邻域像素进行加权平均，在平滑噪声的同时保持图像原本的灰度分布结构特征，在此基础上进一步发展出双边滤波器	对于服从正态分布的噪声有很好的抑制效果，可以很好地降低图片噪声，保留更多的图像细节	去噪的同时也会使得图像变得模糊，对椒盐噪声作用较小
形态学滤波	从传统的形态学开闭运算的角度出发，设计的一种基于形态学原理的滤波方法，如开闭运算、顶帽变换等	可以消除一些小的像素点，且在不改变物体形状的前提下对图像进行平滑操作	对于较大的噪点无法去除
双边滤波^［33］	通过考虑距离因素和像素值差异的影响，使其在去噪的同时，能够很好地保留图像的特征信息	对于噪声不集中、细节较多的图像，能够很好地保护细节信息	去噪的同时，会移除图像纹理，并且会保留图像中的阴影
小波变换	对图像进行多次小波变换，然后将低频信息滤除并对高频信息进行平滑滤除噪声，最后通过逆变换重构原始图像以实现对图像的滤波处理	能够保留图像的频率信息，同时也保留了图像的空间信息	方向性较弱，只能捕捉有限的方向信息，不包含高阶的各向异性因素
连通域去噪	通过设置阈值大小，剔除小于阈值的连通域	可以去除较大的噪点	阈值需要按人为经验设置，且不能去除面积跟裂缝相近的噪点
基于特征去噪	裂缝一般具有明显的线形特征，可将长度小于阈值或者长宽比不满足裂缝特征的去除	可以去除非线形噪点，适用于裂缝去噪	不能去除线形特征较强噪点

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标定方法	优点	缺点	常用方法
传统相机标定法	可使用于任意的相机模型、精度高	需要标定板、结果受标定板精度影响	Tsai两步法、张氏标定法
主动视觉相机标定法	算法简单、鲁棒性高	成本高、设备昂贵	主动系统控制相机做特定运动
相机自标定法	灵活性强、可在线标定	精度低、鲁棒性差	分层逐步标定、基于Kruppa方程

数据集名称	描述	应用领域
Aft_Original_Crack_DataSet_Second^［78］	2 068张1 024×1 024像素的桥梁裂缝图像，全部包含裂缝	语义分割及目标检测
Concrete Crack Images for Classification^［79］	40 000张227×227像素的混凝土表面裂缝图像，部分无裂缝	语义分割及图像分类
SDNET2018^［80］	56 092张256×256像素的桥梁裂缝、缺损、孔洞图像，部分无裂缝	语义分割、目标检测及图像分类
Crack dataset^［81］	6 069张224×224像素的桥梁裂缝图像，全部包含裂缝	语义分割及图像分类
Deep Crack^［82］	8 592张256×256像素的混凝土表面裂缝图像	语义分割

算子	优缺点比较
Roberts	对陡峭的低噪声的图像处理效果较好，当图像边缘接近正负45°时，处理效果最佳，但使用Roberts算子提取的边缘线的比较粗，因此边缘定位不是很准确
Sobel	一阶导数算子，抗噪性能比较好，所以适用于噪声较多的图像，且该算子对边缘定位比较准确^{［87，88］}
Prewitt	对灰度渐变和噪声较多的图像处理效果较好
Log	二阶导数算子，结合了高斯和拉普拉斯算法，边缘检测受噪声影响较大，其适用于背景与裂缝明暗程度差距大且待测面上噪声较小的情况，该算法容易在灰度变化较小的区域检测出伪边缘
Canny	此方法不容易受噪声的干扰，能够检测到真正的弱边缘，该方法的优点在于使用双阈值分别检测强边缘和弱边缘，并且仅当弱边缘与强边缘相连时，才将弱边缘包含在输出图像中^［89］

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