Crack Detection Algorithm Research Articles

Modeling of microscope images for early detection of fatigue cracks in structural materials

From the perspectives of health monitoring and life extension of structural materials, this paper addresses the problem of early detection of fatigue cracks in metallic materials (e.g., polycrystalline alloys). To this end, optical images have been collected from an ensemble of test specimens to construct computationally efficient models of crack evolution; these images are segmented into two major categories. The first category comprises images of (structurally) healthy specimens, while the second category contains images of specimens with cracks, including those in early stages of crack evolution. Based on this information, algorithms for early detection of crack formation are formulated in the setting of image classification, where the bag-of-words (BoW) technique has been used to develop models of the sensed images from a microscope, resulting in computationally efficient crack detection algorithms. To evaluate the performance of these crack detection algorithms, experiments have been conducted on a special-purpose fatigue testing apparatus, equipped with a computer-controlled and computer-instrumented confocal microscope system. The results of experimentation with multiple test specimens show excellent crack detection capabilities when the proposed BoW-based feature extraction is combined with quadratic support vector machine (QSVM) for pattern classification. Comparative evaluation with other classification tools establishes superiority of the proposed BoW/QSVM technique.

Visualization of the Rolling Contact Fatigue Cracks in Rail Tracks with a Magnetooptical Sensor

The rolling contact fatigue cracks (RCF), produced at the surface of the rail tracks, can be detected using a magnetooptical (MO) sensor. Rail tracks are carbon steels with pearlite microstructure. This microstructure has a lamellar texture composed of alternating layers of ferrite and cementite. Both phases are soft ferromagnetic materials at room temperature. If an external magnetic field is applied on the surface of a rail track, the reduced magnetic permeability causes a magnetic leakage field above the cracks. When the external magnetic field is removed, in most cases, a residual stray magnetic field remains above the cracks. When a MO sensor is placed on the surface of the rail track, the sudden change of the stray remanent magnetic field near a crack, yields a significant rotation of the polarization plane of the reflected light, resulting in high MO contrast, exactly above the cracks. Using a polished surface and a cross-section from the head of the rail track, we succeeded in visualizing the RCF cracks in the laboratory. The RCF cracks can also be detected on the surface of the rail track, in field measurements, using a portable commercial polarized light microscope equipped with a MO sensor. Finally, we use computer vision methods, to automatically detect the RCF cracks, using video recorded by displacing the portable microscopy with the MO sensor, on the surface of the rail tracks. We tested an unsupervised automatic crack detection algorithm, which exploits the tubular contrast of the RCF cracks to pinpoint the pixels that correspond to them.

Automatic Pavement Crack Detection by Multi-Scale Image Fusion

Pavement crack detection from images is a challenging problem due to intensity inhomogeneity, topology complexity, low contrast, and noisy texture background. Traditional learning-based approaches have difficulties in obtaining representative training samples. We propose a new unsupervised multi-scale fusion crack detection (MFCD) algorithm that does not require training data. First, we develop a windowed minimal intensity path-based method to extract the candidate cracks in the image at each scale. Second, we find the crack correspondences across different scales. Finally, we develop a crack evaluation model based on a multivariate statistical hypothesis test. Our approach successfully combines strengths from both the large-scale detection (robust but poor in localization) and the small-scale detection (detail-preserving but sensitive to clutter). We analyze and experimentally test the computational complexity of our MFCD algorithm. We have implemented the algorithm and have it extensively tested on three public data sets, including two public pavement data sets and an airport runway data set. Compared with six existing methods, experimental results show that our method outperforms all counterparts. Specifically, it increases the precision, recall, and F1-measure over the state-of-the-art by 22%, 12%, and 19%, respectively, on one public data set.

Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures

This paper summarizes the results of traditional image processing algorithms for detection of defects in concrete using images taken by Unmanned Aerial Systems (UASs). Such algorithms are useful for improving the accuracy of crack detection during autonomous inspection of bridges and other structures, and they have yet to be compared and evaluated on a dataset of concrete images taken by UAS. The authors created a generic image processing algorithm for crack detection, which included the major steps of filter design, edge detection, image enhancement, and segmentation, designed to uniformly compare different edge detectors. Edge detection was carried out by six filters in the spatial (Roberts, Prewitt, Sobel, and Laplacian of Gaussian) and frequency (Butterworth and Gaussian) domains. These algorithms were applied to fifty images each of defected and sound concrete. Performances of the six filters were compared in terms of accuracy, precision, minimum detectable crack width, computational time, and noise-to-signal ratio. In general, frequency domain techniques were slower than spatial domain methods because of the computational intensity of the Fourier and inverse Fourier transformations used to move between spatial and frequency domains. Frequency domain methods also produced noisier images than spatial domain methods. Crack detection in the spatial domain using the Laplacian of Gaussian filter proved to be the fastest, most accurate, and most precise method, and it resulted in the finest detectable crack width. The Laplacian of Gaussian filter in spatial domain is recommended for future applications of real-time crack detection using UAS.

Possibilities and Limitations of Automatic Feature Extraction shown by the Example of Crack Detection in 3D-CT Images of Concrete Specimen

To assess the influence of the alkali-silica reaction (ASR) on pavement concrete 3D-CT imaging has been applied to concrete samples. Prior to imaging these samples have been drilled out of a concrete beam pre-damaged by fatigue loading. The resulting high resolution 3D-CT images consist of several gigabytes of voxels. Current desktop computers can visualize such big datasets without problems but a visual inspection or manual segmentation of features such as cracks by experts can only be carried out on a few slices. A quantitative analysis of cracks requires a segmentation of the whole specimen which could only be done by an automatic feature detection. This arises the question of the reliability of an automatic crack detection algorithm, its certainty and limitations. Does the algorithm find all cracks? Does it find too many cracks? Can parameters of that algorithm, once identified as good, be applied to other samples as well? Can ensemble computing with many crack parameters overcome the difficulties with parameter finding? By means of a crack detection algorithm based on shape recognition (template matching) these questions will be discussed. Since the author has no access to reliable ground truth data of cracks the assessment of the certainty of the automatic crack is restricted to visual inspection by experts. Therefore, an artificial dataset based on a combination of manually segmented cracks processed together with simple image processing algorithms is used to quantify the accuracy of the crack detection algorithm. Part of the evaluation of cracks in concrete samples is the knowledge of the surrounding material. The surrounding material can be used to assess the detected cracks, e.g. micro-cracks within the aggregate-matrix interface may be starting points for cracks on a macro scale. Furthermore, the knowledge of the surrounding material can help to find better parameter sets for the crack detection itself because crack characteristics may vary depending on their surrounding material. Therefore, in addition to a crack detection a complete segmentation of the sample into the components of concrete, such as aggregates, cement matrix and pores is needed. Since such a segmentation task cannot be done manually due to the amount of data, an approach utilizing convolutional neuronal networks stemming from a medical application has been applied. The learning phase requires a ground truth i.e. a segmentation of the components. This has to be created manually in a time-consuming task. However, this segmentation can be used for a quantitative evaluation of the automatic segmentation afterwards. Even though that work has been performed as a short term subtask of a bigger project funded by the German Research Foundation (DFG) this paper discusses problems which may arise in similar projects, too.

Automatic Segmentation and Enhancement of Pavement Cracks Based on 3D Pavement Images

Pavement cracking is a significant symptom of pavement deterioration and deficiency. Conventional manual inspections of road condition are gradually replaced by novel automated inspection systems. As a result, a great amount of pavement surface information is digitized by these systems with a high resolution. With pavement surface data, pavement cracks can be detected using crack detection algorithms. In this paper, a fully automated algorithm for segmenting and enhancing pavement crack is proposed, which consists of four major procedures. First, a preprocessing procedure is employed to remove spurious noise and rectify the original 3D pavement data. Second, crack saliency maps are segmented from 3D pavement data using steerable matched filter bank. Third, 2D tensor voting is applied to crack saliency maps to achieve better curve continuity of crack structure and higher accuracy. Finally, postprocessing procedures are used to remove redundant noises. The proposed procedures were evaluated over 200 asphalt pavement images with diverse cracks. The experimental results demonstrated that the proposed method showed a high performance and could achieve average precision of 88.38%, recall of 93.15%, and F-measure of 90.68%, respectively. Accordingly, the proposed approach can be helpful in automated pavement condition assessment.

A fast adaptive crack detection algorithm based on a double-edge extraction operator of FSM

Surface cracks in concrete structures are critical indicators of structural damage and durability. The vision-based methods can automatically extract crack information from images. Standardizing crack identification using image binarization and region classification, is challenging because of the parameters dependence and high time consumption. This paper presents a fast adaptive crack detection algorithm that has an adaptive binarization procedure without any specific parameter and a machine learning-based classification procedure. Firstly, according to the double edge characteristics of cracks, a finite state machine (FSM) operator is designed. The operator searches valleys and hillsides on the grayscale curve, which are the location of candidate cracks. While the image is processed by the operator, the features of crack regions can be computed directly, which composes the crack samples in the manual marked images. Secondly, a random forest classifier is trained and tested by the samples. Crack detection experiments on concrete components prove that the average detection sensitivity is over 93%, and the time complexity is extremely low that the average processing time of megapixel images is 95 ms.

Reference-Free Breathing Crack Identification of Beam-Like Structures Using an Enhanced Spatial Fourier Power Spectrum with Exponential Weighting Functions

Detection of incipient damage of structures at the earliest possible stage is desirable for successful implementation of any health monitoring system. In this paper, we focus on breathing crack problem and present a new reference-free algorithm for fatigue crack detection, localization, and characterization for beam-like structures. We use the spatial curvature of the Fourier power spectrum as a damage sensitive feature for fatigue crack identification. An exponential weighting function that takes into account nonlinear dynamic signatures, such as sub- and superharmonics, is proposed in the Fourier power spectrum in order to enrich the damage-sensitive features of the structure. Both numerical and experimental studies have been carried out to test and verify the proposed algorithm.

The Algorithm of Concrete Surface Crack Detection Based on the Genetic Programming and Percolation Model

Because of the impact of the variation in different concrete surface images, such as the heterogeneity of the detection environment, uneven illumination, stains, the block, and water leakage, the existing crack detection algorithms cannot detect the real crack quickly and effectively. In this paper, a genetic algorithm based on genetic programming (GP) and percolation model is proposed. This method involves three steps. First, the cracks are pre-extracted by the image processing model of GP. Second, the crack tip is calculated after the crack skeleton is extracted. With the endpoint as the anchor point, high speed, and high precision percolation are used to detect the cracks with small width accurately. Concurrently, the fracture unit areas are scanned for connection. Finally, the pre-extracted cracks are connected with the cracks detected by the percolation, and the mass interference area is removed to obtain the real cracks on the concrete surface. The simulation results show that the concrete surface crack detection algorithm based on the GP and percolation model can effectively combine both of their advantages. The algorithm proposed in this paper can detect real concrete surface cracks accurately and effectively with strong robustness.

РАЗВИТИЕ МЕТОДА КОРРЕЛЯЦИИ ЦИФРОВЫХ ИЗОБРАЖЕНИЙ ДЛЯ ИЗУЧЕНИЯ ПРОЦЕССОВ ДЕФОРМАЦИИ И РАЗРУШЕНИЯ КОНСТРУКЦИОННЫХ МАТЕРИАЛОВ

A number of problems related to the digital image correlation method from the standpoint of both the hardware and software development and testing, as well as solving problems in the field of fatigue fracture mechanics are described. Testing of the developed computer stereo vision system was carried out using a series of stereo pairs reflecting the change in the location of the object in space as well as plane and out-of-plane deformations. It is shown that the error in determining spatial coordinates does not exceed 0.75 units, while the error in computing the strain tensor components in case of a system with a single camera is two orders of magnitude larger than that at using the stereo machine vision system. An algorithm for automatic crack detection on optical images and calculating its tip coordinates was proposed and tested. It is shown that when the frame size is 2000×1000 pixels the coordinates of the crack tip might be determined with an average error of about 56 pixels, while the average error of the crack area determination does not exceed 1.93%. A modified incremental algorithm for calculating displacements on a series of stereo pairs is proposed, which allows one to estimate large magnitude displacements during serial processing of images. An algorithm for measuring the J-integral using the digital image correlation method has been developed. It is shown that the deviation of the calculated J-integral values from the model ones is on average 1.75 %. A quantitative characterization of the fatigue crack growth process in metal alloys was carried out using the technique based on the digital image correlation (in terms of fracture mechanics) including the calculation of the fatigue crack growth rate da / dN , maximum strain (emax) and effective cycle asymmetry ( R eff).

Research on Crack Detection Algorithm of the Concrete Bridge Based on Image Processing

The appearance and development of cracks in the concrete bridge will seriously affect the safe use of bridge buildings. In order to better satisfy the crack detection requirement, this paper comes up with an image preprocessing scheme combining multiple adaptive filtering and contrast enhancement based on the image processing technology of concrete crack, which can improve the removal effect of background noise and obtain the characteristic vein information of tiny cracks. Then we designed a local adaptive algorithm of Otsu threshold segmentation and integrated with modified Sobel operator for removing isolated noise spots, so as to extract the crack edge information and improve the positioning accuracy of the crack boundary. Furthermore, according to the image feature of the bridge crack edge, the target crack is identified as well as classified and the feature data is calculated. The results of case analysis show that the data processing precision of the detection algorithm can reach 0.02mm, which can satisfy the actual engineering detection requirements of concrete bridge crack.

基于Bilateral-Frangi滤波的桥梁裂缝检测算法

基于Bilateral-Frangi滤波的桥梁裂缝检测算法

Crack Detection and Evolution Law for Rock Mass under SHPB Impact Tests

In order to accurately identify and quantitatively calculate the surface cracks of rock mass under SHPB impact loading, an automatic crack detection algorithm was proposed and evaluated by the experiment. In SHPB experiment, cracks on the rock surface can effectively reflect its current state and better analyze the damage process. Firstly, the SHPB system was used to impact 12 groups of rock specimens under different impact velocities. A high‐frame camera with 50,000 FPS was used to capture the damage process of the rock mass; using the manual annotation method, we got a dataset of SHPB damage images including a total of 310 original images and 310 corresponding cracked annotations. Secondly, a deep convolution network model named CrackSHPB was designed based on a deep learning algorithm. The algorithm can automatically identify the crack on the rock surface during impact damage process and further provide a quantitative result of cracks, crack area. Finally, after the crack on the rock surface in each frame image was identified automatically through the model, cracks were quantitatively analyzed by the proposed algorithm, the growth rate of cracks was calculated, and their evolution law was concluded. The crack identification algorithm proposed in this paper can provide a more accurate quantitative method for rock damage by cracks on the rock surface, and evolution law can further explain the failure process of rock at high strain rate.

复杂背景下基于图像处理的桥梁裂缝检测算法

复杂背景下基于图像处理的桥梁裂缝检测算法

Analysis Image-Based Automated 3D Crack Detection for Post-disaster Bridge Assessment in Flyover Mall Boemi Kedaton

Recovery efforts following a disaster can be slow and painstaking work, and potentially put responders in harm's way. A system which helps identify defects in critical building elements (e.g., concrete columns) before responders must enter a structure can save lives. In this paper we propose a system, centered around an image based three-dimensional (3D) reconstruction method and a new 3D crack detection algorithm. The image-based method is capable of detecting and analyzing surface damages in 3D. We also demonstrate how the robotics can be used to gather the images from which the reconstruction is created, further reducing the risk to responders. In this regard, image-based 3D reconstructions represent a convenient method of creating 3D models because most robotic platforms can carry a lightweight camera payload. Additionally, the proposed 3D crack detection algorithm also provides the advantage of being able to operate on 3D mesh models regardless of their data collection source. Our experimental results show that 3D crack detection algorithm performs well constructions, successfully identifying cracks, reconstructing 3D profiles, and measuring geometrical characteristics on damaged elements and not finding any cracks on intact ones.

Three‐Dimensional Asphalt Pavement Crack Detection Based on Fruit Fly Optimisation Density Peak Clustering

Complex pavement texture and noise impede the effectiveness of existing 3D pavement crack detection methods. To improve pavement crack detection accuracy, we propose a 3D asphalt pavement crack detection algorithm based on fruit fly optimisation density peak clustering (FO‐DPC). Firstly, the 3D data of asphalt pavement are collected, and a 3D image acquisition system is built using Gocator3100 series binocular intelligent sensors. Then, the fruit fly optimisation algorithm is adopted to improve the density peak clustering algorithm. Clustering analysis that can accurately detect cracks is performed on the height characteristics of the 3D data of the asphalt pavement. Finally, the clustering results are projected onto a 2D space and compared with the results of other 2D crack detection methods. Following this comparison, it is established that the proposed algorithm outperforms existing methods in detecting asphalt pavement cracks.

A novel asphalt pavement crack detection algorithm based on multi-feature test of cross-section image

A novel asphalt pavement crack detection algorithm based on multi-feature test of cross-section image

Crack detection based on the mesoscale geometric features for visual concrete bridge inspection

False cracks, such as split joints and scratches, have macroscopic geometry that is similar to real cracks, which can influence the crack detection efficiency for a concrete bridge. To solve this problem, a crack detection algorithm based on the mesoscale geometric features of cracks is proposed. Through the mesoscale analysis of concrete crack formation and propagation mechanisms, it is found that a concrete crack propagates at the interface between aggregates and mortar and usually has a meandering path, whereas a false crack’s path is usually smooth or even straight. Thus the path smoothness of a crack candidate is chosen as the detection basis. The algorithm extracts a crack candidate with conventional methods, and also its skeleton for representing the path. In addition, the feature parameters are designed to quantify the path smoothness. Moreover, a back propagation neural network (BPNN) and a support vector machine (SVM) for the classification of crack candidates are trained using the proposed feature parameters as the input. Experimental results show that the classification rate of the BPNN trained by new features is 91.7%, which is better than the BPNNs trained by conventional features. The classification rate of the SVM is 93.3%, which is more suitable for engineering in small size samples.

Concrete surface crack detection with the improved pre-extraction and the second percolation processing methods.

Monitoring the instantaneous and changing concrete surface condition is paramount to cost-effectively managing tunnel assets. In practice, detecting cracks efficiently and accurately is a very challenging task due to concrete blebs, stains, and illumination over the concrete surface. Unclear and tiny cracks cannot be detected effectively. In this paper, we proposed an ultra-efficient crack detection algorithm (CrackHHP) and an improved pre-extraction and second percolation process based on the percolation model to address these issues. Our contributions are shown as follows: 1) apply the overlapping grids and weight-based, redefined pixel value to obtain the candidate dark pixel image while preserving the cracks. 2) introduce the second percolation processing to generate a high-accuracy crack detection algorithm, which can connect the tiny fractures and detect the tiny cracks. 3) construct a high-efficiency and high-accuracy crack detection algorithm combining the improved pre-extraction and the second percolation process. The experimental results demonstrate that CrackHHP can significantly improve the efficiency and accuracy of crack detection.

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

AbstractThe 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.