基于计算机视觉的混凝土结构裂缝检测研究现状与展望

谢明志; 樊丁萌; 蒋志鹏; 邓飞; 王坤; 韩晨; 杨永清

doi:10.3969/j.issn.0258-2724.20240115

基于计算机视觉的混凝土结构裂缝检测研究现状与展望

doi: 10.3969/j.issn.0258-2724.20240115

谢明志^{1, 2,},
樊丁萌^{1, 2},
蒋志鹏^{1, 2},
邓飞^{1, 3},
王坤^{1, 2},
韩晨^{1, 2},
杨永清^{1, 2}

1.
西南交通大学土木工程学院，四川成都 610031
2.
西南交通大学桥梁智能与绿色建造全国重点实验室，四川成都 611756
3.
四川金通工程试验检测有限公司，四川成都 610047

基金项目: 国家自然科学基金项目（52322811）；四川省科技计划资助项目（2020YJ0081）

详细信息

作者简介:
谢明志（1985—），男，副教授，博士，硕士研究生导师，研究方向为桥梁智能检测及损伤评估，E-mail：mzxie@home.swjtu.edu.cn

中图分类号: U446；U24
计量
- 文章访问数: 53
- HTML全文浏览量: 23
- PDF下载量: 9
- 被引次数: 0
出版历程
- 收稿日期: 2024-03-08
- 修回日期: 2024-07-11
- 网络出版日期: 2025-03-29

Research Status and Prospects of Computer Vision-Based Crack Detection of Concrete Structure

XIE Mingzhi^{1, 2
,},
FAN Dingmeng^{1, 2},
JIANG Zhipeng^{1, 2},
DENG Fei^{1, 3},
WANG Kun^{1, 2},
HAN Chen^{1, 2},
YANG Yongqing^{1, 2}

1.
School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China
2.
State Key Laboratory of Bridge Intelligent and Green Construction, Southwest Jiaotong University, Chengdu 611756, Chengdu 611756, China
3.
Sichuan Jintong Engineering Test and Inspection Co., Ltd., Chengdu 610047, China

摘要

摘要:
裂缝检测作为混凝土结构健康监测的重要内容之一，反映了结构受力及损伤状态，其检测及评估是保障结构安全服役的核心技术. 传统的检测方法时空上覆盖范围有限，受环境、高空等因素影响较大，检测效率及精度相对较低，且较依赖于主观判断，易造成漏检与误检. 基于计算机视觉的检测方法通过搭载数字成像设备进行数据采集、输入、图像处理，对混凝土表面进行自动分析和识别，具有高效、准确、客观等优点，在混凝土结构裂缝智能检测领域应用广泛. 从图像采集、图像处理、识别算法和结构评估4个方面详细阐述基于计算机视觉的混凝土裂缝检测原理、方法和应用；综合评述数字成像技术中裂缝图像采集设备及各种图像预处理方法的适用情况，并分析不同识别算法的优缺点及适用性；与此同时，总结凝练当前研究的不足，分析计算机视觉技术在设备智能化、网络轻量化等方向上的应用及研究中面临的挑战和问题，并提出相应的解决措施，从多源数据融合利用、智能设备轻型化、数字成像与裂缝映射、结构评估高效性及实时性等方面进行展望.
- 计算机视觉 /
- 裂缝识别 /
- 智能检测 /
- 混凝土裂缝 /
- 识别算法 /
- 结构评估
Abstract:
As one of the important contents of health monitoring of concrete structure, crack detection reflects the stress and damage state of the structure, and the detection and evaluation is the core technology to ensure structure safety for service. The traditional detection methods have limited coverage in time and space and are greatly affected by environmental and altitude factors, so the detection efficiency and accuracy are relatively low. Additionally, they are dependent on subjective judgment, which is easy to cause missed detection and false detection. The detection method based on computer vision is equipped with digital imaging equipment for data acquisition, input, and image processing to automatically analyze and identify the concrete surface, which has the advantages of high efficiency, accuracy, and objectivity and is widely used in the field of intelligent crack detection of concrete structures. The principle, method, and application of concrete crack detection based on computer vision were described in detail from four aspects: image acquisition, image processing, recognition algorithm, and structure evaluation. Besides, the application of crack image acquisition equipment and various image preprocessing methods in digital imaging technology was reviewed comprehensively, and the advantages, disadvantages, and applicability of different recognition algorithms were analyzed. At the same time, the shortcomings of current research were summarized, and the challenges and problems faced by the application of computer vision technology for equipment intelligence and lightweight network were analyzed. Then the corresponding solutions were proposed. Prospects are also presented from the aspects of multi-source data fusion and utilization, lightweight intelligent equipment, digital imaging and crack mapping, and high-efficiency and real-time structure evaluation.
- computer vision /
- crack identification /
- intelligent detection /
- concrete crack /
- recognition algorithm /
- structural assessment

HTML全文

随着新基建进程的逐步推进和人们环保意识的逐渐提升，特高压电网建设不断对施工效率和成本提出更高的要求. 然而，由于特高压等输电线路路径走廊经常会经过交通不便甚至是人迹罕至的深山老林或崇山峻岭，输电线路建设过程中的物料运输一直是制约电网建设效率和成本的一大因素. 作为一种运输效率高、运输成本低、地形和环境适应能力强的运输方式^[1]，货运专用索道被越来越多地应用于输电线路施工过程中的物料运输中^[2].

为进一步降低货运索道的运输成本，提高运输效率，很多学者尝试对索道结构和设备进行研究和改进. 例如：缪谦等^[3-5]研究了货运索道运输技术与设备；江明等^[6-10]提出了一系列承载索和索道整体结构的方案及计算方法；白雪松等^[11]开发了一种货运索道工作索计算软件，李洋等^[12]设计了一种辅助索道方案优化设计平台. 而索道的架设路径也能够在很大程度上影响货运索道的物料运输效率和成本. 相对于传统的依靠人工现场勘查的索道路径规划方法，索道路径自动规划方法所考虑的备选上下料点、备选索道路径更加全面，而规划所耗费时间短，可大幅减少人工和经济成本. 在路径自动规划研究领域，王刚等^[13]基于改进的A* 算法进行了输电线路路径智能选线研究；刘亮亮^[14]基于蚁群算法构建了超高压输电线路路径选择的规划模型；谢景海等^[15]提出了一种用于输电线路路径搜索的改进蚁群优化算法. 这些研究对货运索道的路径规划研究有较大的借鉴意义，但总体来说，现有关于货运索道的路径自动规划研究较少，且无法实现索道路径的自动规划. 例如李攀等^[16]设计了一种三维GIS辅助山区输电线路货运索道选线系统，但是，由于索道支架位置自动搜索方法的缺失，该辅助选线系统仅实现了索道路径规划选线的半自动化.

因此，本文提出了3种在二维地形剖面上进行索道支架位置自动搜索的方法：凸包点遍历法、地形自适应法和干涉点搜索法，并对这3种方法进行对比分析，提出适用于工程实际的索道支架位置搜索方法，实现输电线路货运索道路径规划的自动化.

1. 索道支架位置自动搜索流程

1.1 索道路径二维地形剖面

搜索输电线路货运索道支架位置时，需要先提供如图1所示的索道路径二维地形剖面数据. 图中坐标轴的原点O是上料点在水平面上的投影点，横坐标s经过下料点在水平面上的投影，表示水平距离，纵坐标z表示高度. 因此，索道路径二维地形剖面数据可以用坐标(s_t, z_t)表示，其中t为地形剖面上的点. 受索道运输技术的限制，索道路径的长度一般不超过3000 m.

图 1 索道路径二维地形剖面数据示意

Figure 1. Schematic of 2D terrain profile data of cableway path

下载: 全尺寸图片幻灯片

1.2 索道承载索曲线

在自身重力、外部载荷和内部张力的共同作用下，相邻两个支架之间（编号为λ和λ + 1）的索道承载索会发生下垂，下垂后的承载索曲线 $g\left( {\lambda ,\lambda + 1} \right)$ 可采用抛物线近似，如式（1）所示^[17].

$- {{\textit{z}}_0} = \frac{{4f{s_0}\left( {l - {s_0}} \right)}}{{{l^2}}} + \frac{C}{l}{s_0} \text{，}$

(1)

式中：z₀为承载索曲线上的点相对曲线起点的高度值；s₀为承载索曲线上的点相对起点的水平距离；l为承载索曲线的终点相对起点的水平距离；C为承载索曲线的终点相对起点的高度值；f为承载索曲线的跨中垂度，其取值范围为0.050l ~ 0.080l，模拟承载索载重情况下，可采用f = 0.065l.

1.3 索道支架位置要求

在进行索道支架位置搜索时，考虑到安全性、适用性和经济性，需要对支架位置提出以下要求：

1）索道的支架位置必须保证承载索曲线与地面间的距离合适，以避免索道运行过程中货物触地；

2）相邻支架间的最大跨距不宜大于400 m；

3）相邻支架间的最小跨距不宜小于20 m；

4）中间支架数量不大于7个；

5）弦倾角小于45°.

1.4 主要流程

如图2所示，在自动搜索索道支架位置时，需要经过以下步骤：

图 2 索道支架位置搜索主要流程

Figure 2. Search process of cableway trestle position

下载: 全尺寸图片幻灯片

步骤1　索道支架位置自动搜索与冗余支架筛除. 根据索道路径二维地形剖面数据和索道承载索曲线方程，初步搜索满足索道支架位置要求1）、2）的索道支架位置，并删除多余支架以实现对支架位置的确定.

步骤2　判断索道合理性. 根据索道支架位置要求3） ~ 5），判断索道支架位置搜索方案是否合理.

2. 索道支架位置自动搜索方法

2.1 凸包点遍历法

如图3所示，凸包点遍历法首先搜索出地形上所有凹度小于零的凸包点，然后在每个凸包点建立支架；再判断相邻支架间的距离，如果所有相邻支架间的距离都小于400 m，则该索道支架位置方案初步可行，后续根据其他条件判断合理性；否则，说明使用本方法在该二维地形剖面上无法建立索道.

图 3 凸包点遍历法原理示意

Figure 3. Schematic of principle of convex-point traversal method

下载: 全尺寸图片幻灯片

在所有凸包点建立支架必然会造成支架数量冗余，因此，凸包遍历法还需要对冗余支架进行筛除. 对支架λ （λ = 1, 2, 3, …）进行冗余支架筛除的步骤如下：

步骤1　如图4（a）所示，找出支架λ右侧且距离小于400 m的所有支架（编号为λ + 1, λ + 2, λ + 3, …, γ，其中，γ为支架λ右侧所有支架的最大支架编号），并令η = λ + 2.

图 4 冗余支架筛除示意

Figure 4. Schematic of deleting redundant trestles

下载: 全尺寸图片幻灯片

步骤2　如图4（b）或图4（c）所示，在支架λ和支架η之间建立承载索曲线 $g\left( {\lambda ,\eta } \right)$ ，并判断曲线是否与地形干涉（当承载索曲线与地形剖面的高度差小于1 m时，认为承载索曲线与地形干涉）：如果干涉（如图4（b）所示），说明支架λ和支架η之间的支架未必是冗余支架，所以直接进入下一步；如果不干涉（如图4（c）所示），说明支架λ和支架η之间的支架都是冗余支架，删除这些冗余支架（如图4（d）所示）并进入下一步.

步骤3　令η = η + 1，重复步骤2，直至η = γ.

2.2 地形自适应法

地形自适应法首先在地形曲线的上方建立一条单跨索道承载索曲线（承载索曲线的起点和终点分别位于上料点和下料点正上方），然后控制索道承载索曲线起点和终点逐渐平移下落，靠近上料点和下料点. 在下落过程中，承载索曲线会与地形发生干涉. 每当干涉发生时，在干涉点的位置为索道添加一个中间支架（干涉点的位置为二维地形与承载索曲线的高度之差最大的位置），阻止干涉发生并使得承载索曲线适应地形，直至承载索曲线起点和终点分别与上料点和下料点重合.

如图5所示，该方法的原理可以通过以下步骤进行解释：

图 5 地形自适应法原理示意

Figure 5. Schematic of principle of terrain adaption method

下载: 全尺寸图片幻灯片

步骤1　在上料点和下料点位置设置a和d两点，建立索道承载索曲线（如图5（a）所示），判断承载索曲线是否与地形干涉. 如果不干涉，那么上料点和下料点之间只需要建立一个单跨索道 $g\left( {a,d} \right)$ 即可；如果干涉，同时向上平移点a和点d，直到承载索曲线与地形之间仅有一个干涉点m （如图5（b）所示）.

步骤2　在该干涉点处设置中间支架m，并分别建立承载索曲线 $g\left( {a,m} \right)$ 和 $g\left( {m,d} \right)$ . 然后，将点a和点d向下移动一定的距离，判断 $g\left( {a,m} \right)$ 和 $g\left( {m,d} \right)$ 与地形是否干涉，并分别找到干涉位置b₁和c₁ （如图5（c）所示）.

步骤3　分别在干涉点处设置新的中间支架b₁和c₁，并分别建立承载索曲线 $g\left( {a,{b_1}} \right)$ 和 $g\left( {{c_1},d} \right)$ ，然后，将点a和点d向下移动一定的距离，判断 $g\left( {a,{b_1}} \right)$ 和 $g\left( {{c_1},d} \right)$ 与地形是否干涉，并分别找到干涉位置b₂和c₂ （如图5（d）所示）. 重复步骤3直至点a和点d分别回到上料点和下料点.

基于地形自适应搜索索道支架位置时，并未限制相邻支架间的距离. 为满足索道相邻支架间的最大跨距不宜大于400 m的要求，需对跨距过大的支架作进一步处理. 如图6所示，首先找出索道路径上间距大于400 m的相邻支架（如图6（a）所示），然后在这一对相邻支架的中点附近的凸包上增加新的支架（如图6（b）所示）. 再找出新增支架和两侧相邻支架间的承载索曲线与地形曲线的所有干涉点，在这些干涉点位置增设新的支架（如图6（c）所示）. 不断循环以上步骤优化跨距过大的相邻支架，直到所有档距都不大于400 m且所有承载索曲线与地形之间都不存在干涉点.

图 6 支架间距优化示意

Figure 6. Schematic of trestle spacing optimization

下载: 全尺寸图片幻灯片

在对跨距大于400 m的相邻支架进行处理时，也会造成支架数量冗余，因此，地形自适应法也需要使用如图4所示的方法对冗余支架进行筛除.

2.3 干涉点搜索法

干涉点搜索法以已知的两个相邻支架（例如首次搜索时的上料点和下料点）为起点和终点，建立一条单跨索道承载索曲线，不断在承载索曲线与地形的最大干涉位置添加中间支架，直到承载索曲线不会与地形发生干涉；同时通过对间距大于400 m的相邻支架进行分段处理，建立满足中间支架搜索原则的货运索道.

如图7所示，该方法的原理可以通过以下步骤进行解释：

图 7 干涉点搜索法原理示意

Figure 7. Schematic of principle of interference-point search method

下载: 全尺寸图片幻灯片

步骤1　在相邻支架λ和支架λ + 1之间建立承载索曲线 $g\left( {\lambda ,\lambda + 1} \right)$ （如图7（a）所示），判断曲线是否与地形干涉.

步骤2　如果 $g\left( {\lambda ,\lambda + 1} \right)$ 与地形干涉（如图7（b）所示），在干涉点处设置新的支架，编号为λ + 1 （并将原编号为λ + 1的支架及其后续支架编号顺次增大）.

步骤3　如果 $g\left( {\lambda ,\lambda + 1} \right)$ 与地形不干涉，但支架λ和支架λ + 1的间距大于400 m （如图7（c）所示），那么就先找到两个支架的中点附近的凸点M，然后分别在支架λ和M点间以及M点和支架λ + 1间建立索道承载索曲线 $g\left( {\lambda ,M} \right)$ 和 $g\left( {M,\lambda + 1} \right)$ ，并分别判断 $g\left( {\lambda ,M} \right)$ 和 $g\left( {M,\lambda + 1} \right)$ 曲线是否与地形干涉.

步骤4　如果只有一条承载索曲线与地形干涉（如图7（d）所示），在相应的那个干涉点处设置新的支架，编号为λ + 1 （并将原编号为λ + 1的支架及其后续支架编号顺次增大）（如图7（e）所示）；如果两条承载索曲线都与地形干涉，在相应的两个干涉点处设置两个新的支架，分别编号为λ + 1和λ + 2 （并将原编号为λ + 1的支架及其后续支架编号顺次增大）；如果两条索道都不与地形干涉，则将M点设置为新的支架，编号为λ + 1 （并将原编号为λ + 1的支架及其后续支架编号顺次增大）.

步骤5　如果 $g\left( {\lambda ,\lambda + 1} \right)$ 与地形不干涉，且这两点的间距小于400 m，那么支架λ和支架λ + 1之间无需设立中间支架.

步骤6　针对所有相邻支架，重复步骤1 ~ 5，直到所有相邻支架间的承载索曲线与地形不干涉，且距离小于400 m.

在对跨距大于400 m的相邻支架进行分段处理时，也会造成支架数量冗余，因此，干涉点搜索法也需要使用如图4所示的方法对冗余支架进行筛除.

3. 索道支架位置自动搜索算例

3.1 索道支架位置自动搜索方法对比研究

针对西南地区的典型地貌，共建立10万个索道路径二维地形剖面，典型特征剖面如图8中黑实线所示. 使用本文所提出的3种方法对这些二维地形剖面的索道支架位置搜索问题进行求解，求解结果样例如图8所示. 为了便于观察，在图中分别将由方法2 （地形自适应法）和方法3 （干涉点搜索法）计算得到的支架和承载索曲线在高度方向进行了平移. 在地形剖面1的计算结果中，仅有方法1 （凸包点遍历法）可以成功提供合理的索道支架位置方案，而方法2和方法3搜索的左起第3个和第4个支架之间的跨距都小于20 m，不能满足支架位置要求. 在地形剖面2 ~ 4的计算结果中，3种方法算得的大部分支架位置基本一致，只有少数支架位置有微小的差异.

图 8 地形剖面支架位置搜索结果样例

Figure 8. Search results for profile examples of trestle position

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表1展示了使用本文提出的3种方法对这10万个二维地形剖面算例进行索道支架位置搜索的成功率和计算时间（计算机处理器：英特尔 Core i7-9700 @ 3.00 GHz 八核，内存为8 GB）.

表 1 本文提出的3种方法的计算结果对比

Table 1. Comparison of results obtained by three proposed methods

项目	方法 1 （凸包点遍历）	方法 2 （地形自适应）	方法 3 （干涉点搜索）
二维地形剖面数/万个	10	10	10
单个地形剖面平均计算时间/ms	6.07	3.59	1.04
成功搜索支架位置的索道数/条	9122	8383	8255
二维地形剖面支架位置搜索成功率/%	9.12	8.38	8.26

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| 显示表格

通过对比发现：这3种方法的支架位置搜索成功率差别不大，但单个地形剖面的平均计算时间差异较大. 方法1的支架位置搜索成功率最高，方法2次之，方法3最低；3种方法的成功率由高到低依次为9.12%、8.38%、8.26%. 方法3对单个地形剖面进行处理的平均时间最短，仅需1.04 ms；方法2次之，需3.59 ms；方法1的时间最长，为6.07 ms. 综合对比之后发现：虽然方法3的支架位置搜索成功率略低于另外两种方法，但其计算速度是方法2的3.5倍，是方法1的5.8倍. 因为3种方法的成功率并没有明显的差异，所以在实际工程应用中，推荐使用支架位置搜索速度最快的方法3.

3.2 各因素对支架位置搜索成功率的影响

通过对这10万个算例的计算结果进行统计分析发现，地形剖面的水平长度、最大高度差和梯度的均方根都对支架位置搜索成功率有较大的影响，图9（a） ~ （c）分别展示了这3个因素对支架位置搜索成功率的影响. 如图9（a）所示，随着地形剖面的水平长度由200 m增加到约1600 m，3种方法的支架位置搜索成功率基本保持一致，均由50%逐渐降低到1%以下. 如图9（b）所示，随着地形剖面的最大高度差由0增加到约240 m，3种方法的支架位置搜索成功率基本保持一致，均由40%逐渐降低到1%以下. 如图9（c）所示，随着地形剖面的梯度的均方根由0.2增加到约0.8，3种方法的支架位置搜索成功率基本保持一致，均由40%逐渐降低到5%左右.

图 9 各因素对支架位置搜索成功率的影响

Figure 9. Influence of various factors on success rate of trestle position search

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4. 结　论

本文提出并对比研究了3种索道支架位置自动搜索方法：凸包点遍历法、地形自适应法和干涉点搜索法. 使用这3种方法对10万个二维地形剖面算例进行索道支架位置搜索，通过对结果进行统计分析发现：凸包点遍历法的求解速度最慢，但求解成功率最高；干涉点搜索法的求解速度最快，其求解成功率最低. 3种方法都能够在提供的二维地形剖面上自动搜索满足要求的索道支架位置，为输电线路货运索道路径自动化规划提供有效支持，进而为降低输电线路专用索道的运输成本和提高运输效率提供有力保障.

为进一步降低输电线路施工物料的运输成本和提高运输效率，后续将以本文提出的3种索道支架位置自动搜索方法为基础，开展输电线路货运索道路径自动规划技术研究；同时结合货运索道设计计算及选型方法研究，实现货运索道的路径规划、设计计算与部件选型一体化技术研究；在此基础上，结合公路、水路等运输路径规划方法，实现输电线路施工物料运输路径的协同规划.

图 1 桥上PRSS无人机示意^[23]

Figure 1. Schematic diagrams of PRSS unmanned aerial vehicle (UAV) on the bridge ^[23]

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图 2 智能检测机器人

Figure 2. Intelligent detection robot

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图 3 水下机器人

Figure 3. Underwater robot

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图 4 环形爬壁机器人系统原理图^[35]

Figure 4. Schematic diagram of circular wall-climbing robot system ^[35]

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图 5 不同类型结构表面的裂缝图像采集^[36]

Figure 5. Crack image acquisition of different types of structural surfaces^[36]

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图 6 基于云点的语义分割^[43]

Figure 6. Semantic segmentation based on point cloud ^[43]

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图 7 图像裁剪与拼接^[59]

Figure 7. Image cropping and stitching ^[59]

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图 8 几种基于深度学习的算法

Figure 8. Several algorithms based on deep learning

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图 9 Skele-Marker方法的图像处理步骤^[76]

Figure 9. Image processing steps of Skele-Marker method ^[76]

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图 10 骨架提取产生的毛刺^[92]

Figure 10. Burrs from skeleton extraction ^[92]

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图 11 4种裂缝宽度测量方法

Figure 11. Four measurement methods for crack width

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图 12 2种裂缝宽度测量方法

Figure 12. Two measurement methods for crack width

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图 13 Benchmarks前10名中基于Transformer的数量

Figure 13. Number of Transformer-based algorithms in the top 10 benchmarks

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图 14 SA-1B与常见的语义分割数据集对比^[98]

Figure 14. Comparison of SA-1B with common semantic segmentation datasets ^[98]

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图 15 基于应力-位移曲线定义的损伤指标^[109]

Figure 15. Damage index based on stress-displacement curve ^[109]

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表 1 图像降噪算法分类

Table 1. Classification of image noise reduction algorithms

算法名称	算法描述	算法优缺点	适用情况
均值滤波^[53]	线性滤波器，利用相邻模板像素的均值代替中心像素值	均值滤波简单、计算方便，但缺点是不能很好地去除噪声点，同时容易破坏图像细节导致图像模糊	适用于轻度噪声的平滑处理，能较好抑制高斯噪声
中值滤波^[54-55]	非线性滤波器，利用相邻模板像素的中值代替中心像素值，减少图像模糊程度；在此基础上提出了自适应中值滤波器等	中值滤波实现简单，计算效率高，但对于较小的结构或细节，可能会造成模糊效果	适用于处理椒盐噪声等离散噪声，但不适合处理大尺寸裂缝图像
高斯滤波^[51]	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等	高斯滤波能较好地消除高斯噪声，但可能会导致图像边缘模糊，同时对大尺寸图像来说计算成本过大	适用于平滑图像并保持图像边缘的情况，能很好抑制高斯噪声
形态学滤波^[52]	从传统的形态学开闭运算的角度出发，设计了一种多角度多结构元素加权组合的基于形态学原理的滤波方法，如开闭运算、顶帽变换等	形态学滤波能够有效地去除图像中的小尺度细节，同时保持图像的主要边缘和结构特征不变，但对较大的噪声去除效果不明显.	适用于去除图像中的斑点噪声或孤立噪声
双边滤波^[56]	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	双边滤波器能够在平滑图像的同时有效地保留图像的边缘信息. 但对于一些细小的纹理和细节，可能会导致失真	适用于平滑图像时保持图像的边缘信息和细节的情况，能很好抑制高斯噪声及随机噪声

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表 2 部分开源的混凝土裂缝数据库

Table 2. Part of open-source concrete crack databases

数据集名称	图像大小/像素	图像数量/张	图像来源	标注类型	下载地址
크랙 Dataset^[64]	1280 × 720	713	混凝土建筑	目标检测	https://universe.roboflow.com/hyegeun/-rxh4q
Concrete crack images for classification^[65]	227 × 227	40000	混凝土建筑	图像分类	https://data.mendeley.com/datasets/5y9wdsg2zt/2
Dataset^[66]	256 × 256	919	混凝土隧道	语义分割	https://pan.baidu.com/s/10W01KQqMS8FFAoRpKl4-Qw密码：e9d2
Bridge crack library 2.0^[45]	256 × 256	23400	混凝土桥梁（GAN 生成）	语义分割	https://doi.org/10.7910/DVN/TUFAJT
Crack dataset^[67]	224 × 224	800	混凝土建筑	语义分割	https://drive.google.com/open?id=1cplcUBmgHfD82YQTWnn1dssK2Z_xRpjx
Crack forest-dataset^[68]	480 × 320	118	混凝土道路	语义分割	https://github.com/cuilimeng/CrackForest-dataset
SDNET2018^[69]	256 × 256	56000	混凝土桥梁、建筑、路面	图像分类	https://digitalcommons.usu.edu/all_datasets/48/
Crack-detection^[70]	224 × 224	6069	混凝土桥梁	图像分类	https://github.com/tjdxxhy/crack-detection

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表 3 不同采集设备对检测精度的影响

Table 3. Influence of different equipment on detection accuracy

图像采集方式	识别精度/mm	应用场景
民用无人机	0.50	价格便宜，小巧灵活，可以高效地完成大面积的裂缝检测，适用各种场景
行业无人机	0.10	价格昂贵，体型较大，适合桥梁、大坝等空旷场景
水下机器人	1.00	适用于水库大坝、海底隧道、桥墩水下部分等水下混凝土结构
智能检测车	0.10	载荷较大，可搭载多个摄像头，主要用于桥梁、隧道等场景
微距摄像头	0.01	测量精度高，但要求近距离拍摄，且检测范围小，适合实验室使用

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