Image‐based crack assessment of bridge piers using unmanned aerial vehicles and three‐dimensional scene reconstruction

Crack assessment of bridge piers using unmanned aerial vehicles (UAVs) eliminates unsafe factors of manual inspection and provides a potential way for the maintenance of transportation infrastructures. However, the implementation of UAV‐based crack assessment for real bridge piers is hindered by several key issues, including the following: (a) both perspective distortion and the geometry distortion by nonflat structural surfaces usually appear on crack images taken by the UAV system from the pier surface; however, these two kinds of distortions are difficult to correct at the same time; and (b) the crack image taken by a close‐range inspection flight UAV system is partially imaged, containing only a small part of the entire surface of the pier, and thereby hinders crack localization. In this paper, a new image‐based crack assessment methodology for bridge piers using UAV and three‐dimensional (3D) scene reconstruction is proposed. First, the data acquisition of UAV‐based crack assessment is discussed, and the UAV flight path and photography strategy for bridge pier assessment are proposed. Second, image‐based crack detection and 3D reconstruction are conducted to obtain crack width feature pair sequences and 3D surface models, respectively. Third, a new method of projecting cracks onto a meshed 3D surface triangular model is proposed, which can correct both the perspective distortion and geometry distortion by nonflat structural surfaces, and realize the crack localization. Field test investigations of crack assessment of a real bridge pier using a UAV are carried out for illustration, validation, and error analysis of the proposed methodology.     

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.     

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.     

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.     

A texture‐Based Video Processing Methodology Using Bayesian Data Fusion for Autonomous Crack Detection on Metallic Surfaces

Regular inspection of the components of nuclear power plants is important to improve their resilience. However, current inspection practices are time consuming, tedious, and subjective: they involve an operator manually locating cracks in metallic surfaces in the plant by watching videos. At the same time, prevalent automatic crack detection algorithms may not detect cracks in metallic surfaces because these are typically very small and have low contrast. Moreover, the existences of scratches, welds, and grind marks lead to a large number of false positives when state‐of‐the‐art vision‐based crack detection algorithms are used. In this study, a novel crack detection approach is proposed based on local binary patterns (LBP), support vector machine (SVM), and Bayesian decision theory. The proposed method aggregates the information obtained from different video frames to enhance the robustness and reliability of detection. The performance of the proposed approach is assessed by using several inspection videos. The results indicate that it is accurate and robust in cases where state‐of‐the‐art crack detection approaches fail. The experiments show that Bayesian data fusion improves the hit rate by 20% and the hit rate achieves 85% with only one false positive per frame.     

Automated crack evaluation of a high‐rise bridge pier using a ring‐type climbing robot

This article proposes a deep learning‐based automated crack evaluation technique for a high‐rise bridge pier using a ring‐type climbing robot. First, a ring‐type climbing robot system composed of multiple vision cameras, climbing robot, and control computer is developed. By spatially moving the climbing robot system along a target bridge pier with close‐up scanning condition, high‐quality raw vision images are continuously obtained. The raw vision images are then processed through feature control‐based image stitching, deep learning‐based semantic segmentation, and Euclidean distance transform–based crack quantification algorithms. Finally, a digital crack map on the region of interest (ROI) of the target bridge pier can be automatically established. The proposed technique is experimentally validated using in situ test data obtained from Jang–Duck bridge in South Korea. The test results reveal that the proposed technique successfully evaluates cracks on the entire ROI of the bridge pier with precision of 90.92% and recall of 97.47%.     

Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks

A number of image processing techniques (IPTs) have been implemented for detecting civil infrastructure defects to partially replace human‐conducted onsite inspections. These IPTs are primarily used to manipulate images to extract defect features, such as cracks in concrete and steel surfaces. However, the extensively varying real‐world situations (e.g., lighting and shadow changes) can lead to challenges to the wide adoption of IPTs. To overcome these challenges, this article proposes a vision‐based method using a deep architecture of convolutional neural networks (CNNs) for detecting concrete cracks without calculating the defect features. As CNNs are capable of learning image features automatically, the proposed method works without the conjugation of IPTs for extracting features. The designed CNN is trained on 40 K images of 256 × 256 pixel resolutions and, consequently, records with about 98% accuracy. The trained CNN is combined with a sliding window technique to scan any image size larger than 256 × 256 pixel resolutions. The robustness and adaptability of the proposed approach are tested on 55 images of 5,888 × 3,584 pixel resolutions taken from a different structure which is not used for training and validation processes under various conditions (e.g., strong light spot, shadows, and very thin cracks). Comparative studies are conducted to examine the performance of the proposed CNN using traditional Canny and Sobel edge detection methods. The results show that the proposed method shows quite better performances and can indeed find concrete cracks in realistic situations.     

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.     

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.     

Image analysis method for crack distribution and width estimation for reinforced concrete structures

Crack observation is important for evaluating the structural performance and safety of reinforced concrete (RC) structures. Most of the existing image-based crack detection methods are based on edge detection algorithms, which detect cracks that are wide enough to present dark areas in the obtained images. Cracks initiate as thin cracks, generally having width less than the width of a pixel in images; such cracks are generally undetectable by edge detection-based methods.An image analysis method is proposed to observe the development and distribution of thin cracks on RC surfaces; it also allows estimation of crack widths. Image matching based on optical flow and subpixel is employed to analyze slight concrete surface displacements. Camera calibration is included to eliminate perspective effects and lens distortion. Geometric transformation is adopted so that cameras do not need to be perpendicular to the observed surface or specified positions. Formulas are proposed to estimate the width of shear crack opening. The proposed method was then applied to a cyclic test of an RC structure. The crack widths and their development analyzed by the image analysis were verified with human inspection in the test. In addition, concrete surface cracks that appeared at a very early stage of the test could be observed by the proposed method before they could be detected by the naked eye. The results thus demonstrate that the proposed image analysis method offers an efficient way applicable not only for structural tests but also for crack-based structural-health-monitoring applications.     

Concrete bridge surface damage detection using a single‐stage detector

Early and timely detection of surface damages is important for maintaining the functionality, reliability, and safety of concrete bridges. Recent advancement in convolution neural network has enabled the development of deep learning‐based visual inspection techniques for detecting multiple structural damages. However, most deep learning‐based techniques are built on two‐stage, proposal‐driven detectors using less complex image data, which could be restricted for practical applications and possible integration within intelligent autonomous inspection systems. In this study, a faster, simpler single‐stage detector is proposed based on a real‐time object detection technique, You Only Look Once (YOLOv3), for detecting multiple concrete bridge damages. A field inspection images dataset labeled with four types of concrete damages (crack, pop‐out, spalling, and exposed rebar) is used for training and testing of YOLOv3. To enhance the detection accuracy, the original YOLOv3 is further improved by introducing a novel transfer learning method with fully pretrained weights from a geometrically similar dataset. Batch renormalization and focal loss are also incorporated to increase the accuracy. Testing results show that the improved YOLOv3 has a detection accuracy of up to 80% and 47% at the Intersection‐over‐Union (IoU) metrics of 0.5 and 0.75, respectively. It outperforms the original YOLOv3 and the two‐stage detector Faster Region‐based Convolutional Neural Network (Faster R‐CNN) with ResNet‐101, especially for the IoU metric of 0.75.     

A Nonballasted Rail Track Slab Crack Identification Method Using a Level‐Set‐Based Active Contour Model

The nonballasted rail tracks have been extensively applied in the new high‐speed railway system development in China, that is, the China Railway Track System (CRTS). However, many defects have been identified during the operation of the CRTS, among which concrete slab crack is recognized as one of the most common, yet critical defects that require accurate identification and timely maintenance attention. Because of the unique outlook of the cracks in nonballasted rail track slabs captured in the survey imagery, direct adaptations of the existing crack extraction methods show dramatically degraded performance. A new automatic crack identification method is developed in this study by employing a region‐based active contour framework with the intensity cluster energy. The proposed method embodies three major contributions, including (1) a heavy penalization energy component that could effectively avoid both under‐ and overevolutions; (2) a multiphase level‐set function that effectively evolves the contours generated with different intensity clusters; and (3) a two‐step implementation of the framework that significantly improves the efficiency. The experimental test evaluates the performance of the proposed method using the data collected on high‐speed rail tracks in Hebei Province, China. The proposed method accurately identifies more than 93.0% of digitized cracks in different crack patterns and challenging backgrounds using a data set consisting of 1,500 synthetic images and 150 actual images. In addition, it shows promising performance in comparison with other popular state‐of‐the‐art crack detection algorithms in terms of accuracy and computational efficiency. The proposed method has demonstrated the promising capacity to support a reliable and efficient nonballasted rail track crack identification and to facilitate the subsequent maintenance cost‐effectively.     

Automatic detection method of cracks from concrete surface imagery using two‐step light gradient boosting machine

Automated crack detection based on image processing is widely used when inspecting concrete structures. The existing methods for crack detection are not yet accurate enough due to the difficulty and complexity of the problem; thus, more accurate and practical methods should be developed. This paper proposes an automated crack detection method based on image processing using the light gradient boosting machine (LightGBM), one of the supervised machine learning methods. In supervised machine learning, appropriate features should be identified to obtain accurate results. In crack detection, the pixel values of the target pixels and geometric features of the cracks that occur when they are connected linearly should be considered. This paper proposes a methodology for generating features based on pixel values and geometric shapes in two stages. The accuracy of the proposed methodology is investigated using photos of concrete structures with adverse conditions, such as shadows and dirt. The proposed methodology achieves an accuracy of 99.7%, sensitivity of 75.71%, specificity of 99.9%, precision of 68.2%, and an F‐measure of 0.6952. The experimental results demonstrate that the proposed method can detect cracks with higher performance than the pix2pix‐based approach. Furthermore, the training time is 7.7 times shorter than that of the XGBoost and 2.3 times shorter than that of the pix2pix. The experimental results demonstrate that the proposed method can detect cracks with high accuracy.     

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.     

Wavelet‐Based Detection of Beam Cracks Using Modal Shape and Frequency Measurements

Abstract: When a structure is subjected to dynamic or static loads, cracks may develop and the modal shapes and frequencies of the cracked structure may change accordingly. Based on this, a new method is proposed to locate beam cracks and to estimate their depths. The fault‐induced modal shape and frequency changes of cracked structures are taken into account to construct a new hybrid crack detection method. The method includes two steps: crack localization and depth estimation. The locations of the cracks are determined by applying the wavelet transform to the modal shape. Using the measured natural frequencies as inputs, the depths of the cracks are estimated from a database established by wavelet finite element method. The effectiveness of the proposed hybrid two‐step method is demonstrated by numerical simulation and experimental investigation of a cantilever beam with two cracks. Our analyses also indicate that the proposed method performed reasonably well at certain level of noise.     

Foreground–background separation technique for crack detection

Current level‐2 condition assessment methods for critical infrastructure assets mostly rely on human visual investigation of visible damages and patterns at the structure surface, which can be a costly, time‐consuming, and subjective exercise in reality. In this article, a novel method for crack detection is proposed via salient structure extraction from textured background. This method first extracts strong edges and distinguishes them from strong textures in a local neighborhood. Then, the spatial distribution of texture features is estimated to detect cracks as salient structures that are not widely spread across the whole image. The outputs from these two key steps are fused to calculate the final structure saliency map for generation of the crack masks. This method was validated on a data set with 704 images and the outcome revealed an average f‐measure of 75% in detecting the concrete cracks that is significantly higher than two other baseline methods.     

Concrete crack detection using context‐aware deep semantic segmentation network

Computer‐vision and deep‐learning techniques are being increasingly applied to inspect, monitor, and assess infrastructure conditions including detection of cracks. Traditional vision‐based methods to detect cracks lack accuracy and generalization to work on complicated infrastructural conditions. This paper presents a novel context‐aware deep convolutional semantic segmentation network to effectively detect cracks in structural infrastructure under various conditions. The proposed method applies a pixel‐wise deep semantic segmentation network to segment the cracks on images with arbitrary sizes without retraining the prediction network. Meanwhile, a context‐aware fusion algorithm that leverages local cross‐state and cross‐space constraints is proposed to fuse the predictions of image patches. This method is evaluated on three datasets: CrackForest Dataset (CFD) and Tomorrows Road Infrastructure Monitoring, Management Dataset (TRIMMD) and a Customized Field Test Dataset (CFTD) and achieves Boundary F1 (BF) score of 0.8234, 0.8252, and 0.7937 under 2‐pixel error tolerance margin in CFD, TRIMMD, and CFTD, respectively. The proposed method advances the state‐of‐the‐art performance of BF score by approximately 2.71% in CFD, 1.47% in TRIMMD, and 4.14% in CFTD. Moreover, the averaged processing time of the proposed system is 0.7 s on a typical desktop with Intel^® Quad‐Core? i7‐7700 CPU@3.6 GHz Processor, 16GB RAM and NVIDIA GeForce GTX 1060 6GB GPU for an image of size 256 × 256 pixels.     

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。     

Automated Pixel‐Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep‐Learning Network

The CrackNet, an efficient architecture based on the Convolutional Neural Network (CNN), is proposed in this article for automated pavement crack detection on 3D asphalt surfaces with explicit objective of pixel‐perfect accuracy. Unlike the commonly used CNN, CrackNet does not have any pooling layers which downsize the outputs of previous layers. CrackNet fundamentally ensures pixel‐perfect accuracy using the newly developed technique of invariant image width and height through all layers. CrackNet consists of five layers and includes more than one million parameters that are trained in the learning process. The input data of the CrackNet are feature maps generated by the feature extractor using the proposed line filters with various orientations, widths, and lengths. The output of CrackNet is the set of predicted class scores for all pixels. The hidden layers of CrackNet are convolutional layers and fully connected layers. CrackNet is trained with 1,800 3D pavement images and is then demonstrated to be successful in detecting cracks under various conditions using another set of 200 3D pavement images. The experiment using the 200 testing 3D images showed that CrackNet can achieve high Precision (90.13%), Recall (87.63%) and F‐measure (88.86%) simultaneously. Compared with recently developed crack detection methods based on traditional machine learning and imaging algorithms, the CrackNet significantly outperforms the traditional approaches in terms of F‐measure. Using parallel computing techniques, CrackNet is programmed to be efficiently used in conjunction with the data collection software.