A Pavement Crack Detection Method Combining 2D with 3D Information Based on Dempster‐Shafer Theory

Pavement cracking is one of the main distresses presented in the road surface. Objective and accurate detection or evaluation for these cracks is an important task in the pavement maintenance and management. In this work, a new pavement crack detection method is proposed by combining two‐dimensional (2D) gray‐scale images and three‐dimensional (3D) laser scanning data based on Dempster‐Shafer (D‐S) theory. In this proposed method, 2D gray‐scale image and 3D laser scanning data are modeled as a mass function in evidence theory, and 2D and 3D detection results for pavement cracks are fused at decision‐making level. The experimental results show that the proposed method takes advantage of the respective merits of 2D images and 3D laser scanning data and therefore improves the pavement crack detection accuracy and reduces recognition error rate compared to 2D image intensity‐based methods.     

基于图像局部网格特征的隧道衬砌裂缝自动识别

 裂缝是隧道衬砌最常见也是最严重的病害之一。针对常规图像识别方法存在的问题,提出一种基于图像局部网格特征的隧道衬砌裂缝自动识别方法。首先将图像划分为8 Pixel×8 Pixel的局部网格,基于局部网格内不同方向之间的亮度差异和对比度差异特征设计十字形模板,通过模板计算将网格中灰度值最小的像素识别为潜在的裂缝种子,最后采用种子连接算法将离散的裂缝种子像素连接成为完整的连续裂缝,在连接过程中自动计算裂缝的走向、长度和宽度。通过接受者操作特征曲线估计算法的最优参数和最佳阈值,从定性和定量分析两方面验证其可靠性和准确性。工程应用实例表明,算法能取得良好的裂缝识别效果,特别是对细微裂缝和存在渗漏水的衬砌图像,算法的可靠性和识别率均高于常规的图像识别方法。     

基于计算机视觉技术的结构表面裂缝检测方法研究

计算机视觉技术用于混凝土结构表面裂缝检测,具有现场检测方便、效率高、客观性强的特点,但图像数据分析是该技术的核心,其中裂缝提取与定量测量较为复杂。为提高裂缝图像处理效率和准确率,将深度学习和数字图像处理技术相结合,提出一种裂缝检测方法。建立基于深度卷积神经网络的裂缝识别模型,在图像上自动定位裂缝并结合图像局域阈值分割方法提取裂缝。在裂缝宽度定量测量方面,采用双边滤波算法和三段线性变换对裂缝图像进行预处理,提高了裂缝边缘识别的精确度。通过改进边缘梯度法,实现裂缝最大宽度的定位和裂缝最大宽度的自动获取。该研究为全自动识别裂缝图像及高精度测量裂缝宽度提供了一种解决方法。     

复杂环境公路隧道裂缝快速识别与分割算法研究

随着我国公路隧道由建设为主朝建养并重转化,在运营里程快速增长与既有隧道劣化加剧的双重作用下,移动检测及结构安全快速诊断已成为目前公路隧道运营维养领域的研究热点之一。我国已研发了多种类型的隧道检测车,为裂缝、渗漏水等表观病害的快速检测提供了手段,然而公路隧道衬砌图像背景复杂、干扰因素多、裂缝占比小的特点,给检测数据的快速分析带来巨大挑战,已成为制约技术推广的主要瓶颈。基于深度学习算法,本文提出了一种将目标识别与语义分割进行组合的裂缝快速提取方法,首先采用Faster R-CNN网络对原始衬砌图像进行目标识别,判定所采集图片是否存在裂缝并智能框选出裂缝区域;随后对框选出的裂缝区域自动裁切,由此过滤不含裂缝的图片并去除含裂缝图片中的干扰背景,再利用U-Net语义分割网络对裂缝进行像素级分割。通过实际工程验证发现,单幅图像裂缝整体分割时间小于0.15 s,在常见各类干扰因素下,目标识别F1值可达到92%,语义分割像素准确度可达到98%以上。与阈值分割和同类智能分割算法相比,本方法显著提高了识别速度与精度,为从隧道检测车海量数据中进行快速准确的裂缝提取提供了良好手段。     

Encoder–decoder network for pixel‐level road crack detection in black‐box images

Timely monitoring of pavement cracks is essential for successful maintenance of road infrastructure. Accurate information concerning crack location and severity enables proactive management of the infrastructure. Black‐box cameras, which are becoming increasingly widespread at an affordable price, can be used as efficient road‐image collectors over a wide area. However, the cracks in these images are difficult to detect, because the images containing them often include objects other than roads. Thus, we propose a pixel‐level detection method for identifying road cracks in black‐box images using a deep convolutional encoder–decoder network. The encoder consists of convolutional layers of the residual network for extracting crack features, and the decoder consists of deconvolutional layers for localizing the cracks in an input image. The proposed network was trained on 427 out of 527 images extracted from black‐box videos and tested on the remaining 100 images. Compared with VGG‐16, ResNet‐50, ResNet‐101, ResNet‐200 with transfer learning, and ResNet‐152 without transfer learning, ResNet‐152 with transfer learning exhibited the best performance, achieving recall, precision, and intersection of union of 71.98%, 77.68%, and 59.65%, respectively. The experimental results prove that the proposed method is optimal for detecting cracks in black‐box images at the pixel level.     

Image quality evaluation method for surface crack detection based on standard test chart

The use of automated equipment for surface crack detection based on digital image acquisition is becoming increasingly popular in the inspection industry. While researchers typically focus on improving the accuracy of recognition methods, the image quality is essential to the effectiveness of the algorithm. However, evaluating the quality of crack images has received little attention in computer-aided civil and infrastructure engineering. A prominent issue is whether surface cracks are visible and measurable in images. This study proposes an image quality evaluation method using an original standard test chart that simulates cracks of different widths and directions. Geometric transformations and preprocessing techniques are employed in a full-reference strategy to process the acquired crack images. The resulting information provides quantitative scores for crack visibility and measurability. The proposed Crack Structural Similarity Index is more in line with human perception and offers an accurate evaluation of real image quality. The study shows that Gaussian blur disturbance and random noise disturbance primarily affect measurability and visibility, respectively. Furthermore, the study finds that the quality of the crack image improves with increasing sensor pixel size and using a prime lens over a zoom or long zoom lens. This approach enables comparing image quality collected by different devices in the field environment and provides guidance for optimizing field acquisition parameters. In the future, the results of this study can be applied to facilitate the application of automated testing equipment and improve overall performance.     

Monitoring Crack Changes in Concrete Structures

Abstract:   This study proposes a crack-monitoring system to quantify the change of cracks from multitemporal images during the monitoring period. A series of images were taken from an off-the-shelf digital camera. Concrete cracks were extracted from the digital images by employing a series of image-processing techniques. The image coordinates and orientation of same cracks can be changed since the position and direction of the portable camera vary at every exposure time. To monitor the crack changes (width and length), it is critical to transform the image coordinates of cracks extracted from each image into the same object coordinates of the concrete surface. In this study, such a geometric relationship was automatically recovered using the two-dimensional (2D) projective transformation based on the modified iterated Hough transform (MIHT) algorithm, the result of which solved the transformation parameters. To improve the computational operation of MIHT, regions of parameter estimation were also investigated. The developed algorithms were applied to monitor the crack of the concrete specimen. As a result, the change of cracks on the concrete specimen was successfully detected and accurately quantified.     

Efficient data dimensionality reduction method for improving road crack classification algorithms

Automatic crack classification plays an essential role in road maintenance. Using many features for the classification is inefficient for implementing embedded systems with low computational resources makes it difficult. Therefore, this work proposes a new data dimensionality reduction (DDR) for crack classification algorithms (DDR4CC). DDR4CC reduces the required information about the cracks to only four features. Using these features, the images can be classified into longitudinal, transverse, and alligator cracks or healthy pavement. DDR4CC is compared with eight DDR methods, and the reduced set of features is analyzed using five different classification algorithms. Besides, five different datasets, generated by a combination of several public datasets, are used. We are proposing a simple DDR method with high interpretability of the data, obtaining very fast computation and high accuracy. Experiments show that DDR4CC enhances the results of the classification algorithms, providing almost perfect classifiers with a minimum computation time.     

A grid-based classification and box-based detection fusion model for asphalt pavement crack

Crack identification is essential for the preventive maintenance of asphalt pavement. Through periodic inspection, the characteristics of existing and emerging cracks can be obtained, and the pavement condition index can be calculated to assess pavement health. The most common method to estimate the area of cracks in an image is to count the number of grid cells or boxes that cover the cracks in an image. Accurate and efficient crack identification is the premise of crack assessment. However, the current patch-based classification method is limited by the receptive field and cannot be used to directly classify cracks. Furthermore, the postprocessing algorithm in anchor-based detection is not explicitly optimized for crack topology. In this paper, a new model, which is the fusion of grid-based classification and box-based detection based on You Only Look Once version 5 (YOLO v5) is developed and described for the first time. The accuracy and efficiency of the model are high partly due to the implementation of a shared backbone network, multi-task learning, and joint training. The non-maximum suppression (NMS)–area-reduction suppression (ARS) algorithm is presented to filter redundant, overlapping prediction boxes in the postprocessing stage for the crack topology, and the mapping and matching algorithm is proposed to combine the advantages of both the grid-based and box-based models. A double-labeled dataset containing tens of thousands of asphalt pavement images is assembled, and the proposed method is verified on the test set. The fusion model has superior performance over the individual classification and detection models, and the proposed NMS-ARS algorithm further improves the detection accuracy. Experimental results demonstrate that the presented method effectively realizes automatic crack identification for asphalt pavement.     

基于图象子块分布特性的路面破损图象特征提取

由于路面破损形式的多种多样,造成路面破损分类[1]成为一大难题,这极大的限制了路面破损自动检测的普及和发展,使得路面破损自动检测即使在发达国家也普及得不够理想。本文在前文提出的破损密度因子的基础上,进一步设计了出方向密度因子,得到一种基于图象子块分布特性的路面破损识别算法。通过仿真,验证了其对常见的5种路面破损类型进行分类的可行性。为了进一步验证我们提出的识别算法,论文选择了另外一种路面破损分类算法,即PROXIMITY算法进行神经网络仿真对比。神经网络的训练样本是两组,测试样本也是两组,进行了四次仿真对比。四次仿真结果都显示方向密度因子算法优于PROXIMITY算法。     

Zernike‐moment measurement of thin‐crack width in images enabled by dual‐scale deep learning

Although crack inspection is a routine practice in civil infrastructure management (especially for highway bridge structures), it is time‐consuming and safety‐concerning to trained engineers and costly to the stakeholders. To automate this in the near future, the algorithmic challenge at the onset is to detect and localize cracks in imagery data with complex scenes. The rise of deep learning (DL) sheds light on overcoming this challenge through learning from imagery big data. However, how to exploit DL techniques is yet to be fully explored. One primary component of practical crack inspection is that it is not merely detection via visual recognition. To evaluate the potential risk of structural failure, it entails quantitative characterization, which usually includes crack width measurement. To further facilitate the automation of machine‐vision‐based concrete crack inspection, this article proposes a DL‐enabled quantitative crack width measurement method. In the detection and mapping phase, dual‐scale convolutional neural networks are designed to detect cracks in complex scene images with validated high accuracy. Subsequently, a novel crack width estimation method based on the use of Zernike moment operator is further developed for thin cracks. The experimental results based on a laboratory loading test agree well with the direct measurements, which substantiates the effectiveness of the proposed method for quantitative crack detection.     

An integration–competition network for bridge crack segmentation under complex scenes

The segmentation accuracy of bridge crack images is influenced by high-frequency light, complex scenes, and tiny cracks. Therefore, an integration–competition network (complex crack segmentation network [CCSNet]) is proposed to address these problems. First, a grayscale-oriented adjustment algorithm is proposed to solve the high-frequency light problem. Second, an integration–competition mechanism is proposed to detach complex backgrounds and grayscale features of cracks. Finally, a tiny attention mechanism is proposed to extract the shallow features of tiny cracks. CCSNet outperforms seven state-of-the-art crack segmentation methods in both generalization and comparison experiments on self-built dataset and four public datasets. It also achieved excellent performance in practical bridge crack tests. Therefore, CCSNet is an effective auxiliary method for lowering the cost of bridge safety detection.     

Encoder–decoder with pyramid region attention for pixel-level pavement crack recognition

Timely and accurate extraction of pavement crack information is crucial to maintain service conditions and structural safety for infrastructures and reduce further road maintenance costs. Currently, deep learning techniques for automated pavement crack detection are far superior to traditional manual approaches in both speed and accuracy. However, existing deep learning models may easily lose crack details when processing images containing complex background textures or other noises. Although many studies have alleviated this challenge by introducing attention mechanisms, especially the non-local (NL) block, which has the ability to efficiently capture long-range dependencies to facilitate crack pixel capture, the huge computational cost of NL makes the inference time of the model too long, which is not conducive to practical implementation. In this study, a new module, namely, the pyramid region attention module (PRAM), was developed by combining the pyramid pooling module in the pyramid scene parsing network and optimized NL, which can achieve global multi-scale context integration and long-range dependencies capture at a relatively lower computational cost. By applying PRAM to deep skip connections in the modified U-Net, an effective crack segmentation model called CrackResU-Net was developed. The test results on the existing CrackForest dataset showed that CrackResU-Net not only achieved an F1 score of 0.9580 but also took only 25.89 ms to process an image with a resolution of 480 × 320, which had advantages in accuracy and speed, compared with several other state-of-the-art crack segmentation approaches. It was fully demonstrated that this approach could realize automatic fast and high-precision recognition of pavement cracks for engineering purposes.     

Pixel-level thin crack detection on road surface using convolutional neural network for severely imbalanced data

Because roads are the major backbone of the transportation network, research about crack detection on road surfaces has been popular in computer and infrastructure engineering. When training a convolutional neural network (CNN) for pixel-level road crack detection, three common challenges include (1) the data are severely imbalanced, (2) crack pixels can be easily confused with normal road texture and other visual noises, and (3) there are many unexplainable characteristics regarding the CNN itself. When it comes to very fine and thin cracks, these challenges are exaggerated and a new challenge is introduced, as there can be a discrepancy between the actual width and the annotated width of a crack. To tackle all these challenges of thin crack detection, this paper proposes a new variant of CNN named ThinCrack U-Net, designed to provide thin results upon pixel-level crack detection on road surfaces. The main contribution is to demystify how pixel-level thin crack detection results are affected by different loss functions as well as various combinations of the U-Net components. The experimental results show that ThinCrack U-Net yields a significant performance boost in CrackTree260, from 65.71% to 94.48% F-measure, compared to the existing variant of U-Net previously proposed in the context of pixel-level thin crack detection. Finally, this paper locates the source of undesirable result thickness and solves it with the balanced usage of downsampling/upsampling layers and atrous convolution. Unlike suggested by previous works, different loss functions show no significant impact on ThinCrack U-Net, whereas normalization layers are proved crucial in pixel-level thin crack detection.     

基于机器视觉的高铁隧道衬砌裂缝骨架拐点识别

裂缝作为隧道工程衬砌结构中最为普遍的缺陷,具有不断生长延展的特性。以单裂缝为研究主题,采用基于机器视觉的检测系统对同一裂缝不同时期的高效识别可掌握病害发展状况并制定相应的整改措施。因此本文基于裂缝本身特征研究,提出一种裂缝骨架拐点识别的方法。采用改进的八方向Freeman链码技术,在对可疑拐点进行初步识别后,进行伪拐点剔除,得到真实拐点位置。试验证明,针对不同形态的裂缝图像,该算法具有良好的适应性,拐点剔除率达82%～96%,保留了真实有效的拐点。所提取的拐点前后线段长度比值及拐角作为裂缝特征,具有“基因”属性,该特征可用于匹配原始裂缝和延展裂缝,实现对同一裂缝不同时间点的精确定位。     

CT实验条件下砂岩破裂分形特性研究

 利用单轴压缩条件下砂岩CT资料,对有裂纹出现的s = 20.59～31.03 MPa阶段的CT图像利用所设计的裂纹提取滤波器进行裂纹提取操作,其中提取阈值,是在进行敏感性分析后选定的。在所取得的裂纹二值化图像的基础上,利用码尺法、盒计数法、小岛法和Sandbox法对各载荷步下裂纹面积和长度的分维数进行了计算。最后,对裂纹分维数随加载过程的变化进行分析,利用分维数的演化信息讨论砂岩破裂的分形特性。     

Theoretical and numerical studies of crack initiation and propagation in rock masses under freezing pressure and far-field stress

Water-bearing rocks exposed to freezing temperature can be subjected to freezeethaw cycles leading tocrack initiation and propagation, which are the main causes of frost damage to rocks. Based on theGriffith theory of brittle fracture mechanics, the crack initiation criterion, propagation direction, andcrack length under freezing pressure and far-field stress are analyzed. Furthermore, a calculation methodis proposed for the stress intensity factor （SIF） of the crack tip under non-uniformly distributed freezingpressure. The formulae for the crack/fracture propagation direction and length of the wing crack underfreezing pressure are obtained, and the mechanism for coalescence of adjacent cracks is investigated.In addition, the necessary conditions for different coalescence modes of cracks are studied. Using thetopology theory, a new algorithm for frost crack propagation is proposed, which has the capability todefine the crack growth path and identify and update the cracked elements. A model that incorporatesmultiple cracks is built by ANSYS and then imported into FLAC3D. The SIFs are then calculated using aFISH procedure, and the growth path of the freezing cracks after several calculation steps is demonstratedusing the new algorithm. The proposed method can be applied to rocks containing fillings such asdetritus and slurry.
2014 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting byElsevier B.V. All rights reserved.     

Automating the crack map detection process for machine operated crack sealer

Automated crack-sealing machinery must accurately locate continuous individual crack segments so that they can be processed and treated effectively. In this paper, we illustrate the use of a geodesic minimal path based method for generating the crack map suitable for the path planning process. The user can detect continuous cracks that extend over several miles just by providing the starting point of a crack as an input to the algorithm. The algorithm can also detect transverse cracks by giving a single point on the crack. The continuous crack map generated can be utilized very efficiently to generate the optimal path for the crack sealer. An extensive qualitative and quantitative evaluation on real pavement images was done to show the usefulness of the algorithm. The algorithm is computationally fast and efficient.     

Beamlet Transform‐Based Technique for Pavement Crack Detection and Classification

Abstract: This article presents a Beamlet transform‐based approach to automatically detect and classify pavement cracks in digital images. The proposed method uses a pavement distress image enhancement algorithm to correct the nonuniform background illumination by calculating the multiplicative factors that eliminate the background lighting variation. To extract linear features such as surface cracks from the pavement images, the image is partitioned into small windows and a Beamlet transform‐based algorithm is applied. The crack segments are then linked together and classified into four types: vertical, horizontal, transversal, and block. Simulation results show that the method is effective and robust in the extraction of cracks on a variety of pavement images.     

非贯通裂隙介质中波传播特性试验研究

 以研究非贯通裂隙对波传播的影响为主要目标,采用类岩石材料模型试件模拟节理裂隙介质,对不同裂隙长度、裂隙厚度、裂隙倾角、裂隙密度、裂隙形态(连续与非连续)的非贯通裂隙试件的声波特性进行超声波试验研究,测试和分析纵波在裂隙介质中的波速变化和振幅衰减规律。以国际大型的岩土工程分析程序FINAL为数值分析平台,进行与模型试验相对应的裂隙介质中的波传播数值仿真分析,对比分析纵波波速和波幅衰减的变化规律。研究结果表明,波速随裂隙长度变化很不明显;波幅的衰减取决于与波传播方向相垂直方向的裂隙水平投影长度,裂隙水平投影长度越大,波幅衰减量越大;随裂隙排数的增加,波幅发生衰减,2排裂隙试件的波幅值约为1排裂隙波幅值的1/2,3排裂隙试样的波幅值约为1排裂隙波幅值的1/3,且随裂隙增加,最大波幅值发生滞后现象。