scholarly journals Image-Based Crack Detection Using Crack Width Transform (CWT) Algorithm

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 60100-60114 ◽  
Author(s):  
Hyunwoo Cho ◽  
Hyuk-Jin Yoon ◽  
Ju-Yeong Jung
Keyword(s):  
Crack Width ◽  
Crack Detection

scholarly journals Automatic Tunnel Crack Detection Based on U-Net and a Convolutional Neural Network with Alternately Updated Clique

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 717 ◽  
Author(s):  
Gang Li ◽  
Biao Ma ◽  
Shuanhai He ◽  
Xueli Ren ◽  
Qiangwei Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crack Width ◽  
Crack Detection ◽  
Detection Method ◽  
Learning Algorithm ◽  
Semantic Segmentation ◽  
Fully Convolutional Networks ◽  
Deep Learning Algorithm ◽  
Mean Width

Regular crack inspection of tunnels is essential to guarantee their safe operation. At present, the manual detection method is time-consuming, subjective and even dangerous, while the automatic detection method is relatively inaccurate. Detecting tunnel cracks is a challenging task since cracks are tiny, and there are many noise patterns in the tunnel images. This study proposes a deep learning algorithm based on U-Net and a convolutional neural network with alternately updated clique (CliqueNet), called U-CliqueNet, to separate cracks from background in the tunnel images. A consumer-grade DSC-WX700 camera (SONY, Wuxi, China) was used to collect 200 original images, then cracks are manually marked and divided into sub-images with a resolution of 496   ×   496 pixels. A total of 60,000 sub-images were obtained in the dataset of tunnel cracks, among which 50,000 were used for training and 10,000 were used for testing. The proposed framework conducted training and testing on this dataset, the mean pixel accuracy (MPA), mean intersection over union (MIoU), precision and F1-score are 92.25%, 86.96%, 86.32% and 83.40%, respectively. We compared the U-CliqueNet with fully convolutional networks (FCN), U-net, Encoder–decoder network (SegNet) and the multi-scale fusion crack detection (MFCD) algorithm using hypothesis testing, and it’s proved that the MIoU predicted by U-CliqueNet was significantly higher than that of the other four algorithms. The area, length and mean width of cracks can be calculated, and the relative error between the detected mean crack width and the actual mean crack width ranges from −11.20% to 18.57%. The results show that this framework can be used for fast and accurate crack semantic segmentation of tunnel images.

scholarly journals Construction of Concrete Surface Crack Recognition Model Based on Digital Image Processing Technology

2021 ◽  
Vol 2074 (1) ◽  
pp. 012067
Author(s):  
Yuzhong Kang ◽  
Aimin Yu ◽  
Wenquan Zeng
Keyword(s):  
Image Processing ◽  
Prestressed Concrete ◽  
Crack Width ◽  
Crack Detection ◽  
Concrete Surface ◽  
Concrete Bridges ◽  
Crack Identification ◽  
Detection Accuracy ◽  
Depth Analysis ◽  
Class B

Abstract In this paper, the bridge crack detection method based on digital images is studied. In-depth analysis and evaluation are performed on the image processing algorithms such as image graying, resolution of checkerboard corner pixel rate, filtering denoising, and edge detection, etc. The calculation and software system for bridge crack width based on videos (or images) is implemented, and 15 bridge crack images are used to verify its crack detection accuracy. The results suggest that the proposed crack identification method in this paper can be used for the crack detection of reinforced concrete bridges and class B prestressed concrete bridges properly. When the crack width is greater than 0.3 mm, the calculated crack width value based on images is very close to the measured value.

scholarly journals Novel Electromagnetic Sensors Embedded in Reinforced Concrete Beams for Crack Detection

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5175 ◽  
Author(s):  
Michaela Gkantou ◽  
Magomed Muradov ◽  
George S. Kamaris ◽  
Khalid Hashim ◽  
William Atherton ◽  
...  
Keyword(s):  
Reinforced Concrete ◽  
Crack Width ◽  
Crack Detection ◽  
Low Cost ◽  
Split Ring Resonator ◽  
Reinforced Concrete Beams ◽  
Electromagnetic Signal ◽  
European Standards ◽  
Displacement Transducers ◽  
Microwave Sensors

This paper investigates the possibility of applying novel microwave sensors for crack detection in reinforced concrete structures. Initially, a microstrip patch antenna with a split ring resonator (SRR) structure was designed, simulated and fabricated. To evaluate the sensor’s performance, a series of structural tests were carried out and the sensor responses were monitored. Four reinforced concrete (RC) beam specimens, designed according to the European Standards, were tested under three-point bending. The load was applied incrementally to the beams and the static responses were monitored via the use of a load cell, displacement transducers and crack width gauges (Demec studs). In parallel, signal readings from the microwave sensors, which were employed prior to the casting of the concrete and located along the neutral axis at the mid-span of the beam, were recorded at various load increments. The microwave measurements were analysed and compared with those from crack width gauges. A strong linear relationship between the crack propagation and the electromagnetic signal across the full captured spectrum was found, demonstrating the technique’s capability and its potential for further research, offering a reliable, low-cost option for structural health monitoring (SHM).

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

2019 ◽  
Vol 4 (2) ◽  
pp. 19 ◽  
Author(s):  
Dorafshan ◽  
Thomas ◽  
Maguire
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Frequency Domain ◽  
Crack Width ◽  
Crack Detection ◽  
Spatial Domain ◽  
Gaussian Filter ◽  
Laplacian Of Gaussian Filter ◽  
Image Processing Algorithms ◽  
Processing Algorithms

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.

scholarly journals Image-Based Automated Width Measurement of Surface Cracking

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7534
Author(s):  
Miguel Carrasco ◽  
Gerardo Araya-Letelier ◽  
Ramiro Velázquez ◽  
Paolo Visconti
Keyword(s):  
Crack Width ◽  
Crack Detection ◽  
Construction Materials ◽  
Structural Safety ◽  
Surface Cracking ◽  
Monitoring Task ◽  
Automated Method ◽  
Subjective Judgement ◽  
Width Measurement ◽  
Civil Engineering Infrastructure

The detection of cracks is an important monitoring task in civil engineering infrastructure devoted to ensuring durability, structural safety, and integrity. It has been traditionally performed by visual inspection, and the measurement of crack width has been manually obtained with a crack-width comparator gauge (CWCG). Unfortunately, this technique is time-consuming, suffers from subjective judgement, and is error-prone due to the difficulty of ensuring a correct spatial measurement as the CWCG may not be correctly positioned in accordance with the crack orientation. Although algorithms for automatic crack detection have been developed, most of them have specifically focused on solving the segmentation problem through Deep Learning techniques failing to address the underlying problem: crack width evaluation, which is critical for the assessment of civil structures. This paper proposes a novel automated method for surface cracking width measurement based on digital image processing techniques. Our proposal consists of three stages: anisotropic smoothing, segmentation, and stabilized central points by k-means adjustment and allows the characterization of both crack width and curvature-related orientation. The method is validated by assessing the surface cracking of fiber-reinforced earthen construction materials. The preliminary results show that the proposal is robust, efficient, and highly accurate at estimating crack width in digital images. The method effectively discards false cracks and detects real ones as small as 0.15 mm width regardless of the lighting conditions.

scholarly journals SENSOR EVALUATION FOR CRACK DETECTION IN CONCRETE BRIDGES

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1107-1114
Author(s):  
D. Merkle ◽  
A. Schmitt ◽  
A. Reiterer
Keyword(s):  
Crack Width ◽  
Crack Detection ◽  
Low Cost ◽  
Laser Scanner ◽  
Point Clouds ◽  
Concrete Bridges ◽  
Outdoor Environment ◽  
Laser Triangulation ◽  
Camera Systems ◽  
3D Information

Abstract. Bridges are one of the most critical traffic infrastructure objects, therefore it is necessary to monitor them at regular intervals. Nowadays, this monitoring is made manually by visual inspection. In recent projects, the authors are developing automated crack detection systems to support the inspector. In this pre-study, different sensors, like different camera systems for photogrammetry, a laser scanner, and a laser triangulation system are evaluated for crack detection based on a defined required minimum crack width of 0.2 mm. The used test object is a blasted concrete plate, sized 70 cm × 70 cm × 5 cm and placed in an outdoor environment. The results of the data acquisition with the different sensors are point clouds, which make the results comparable. The point cloud from the chosen laser scanner is not sufficient for the required crack width even at a low speed of 1 m/s. The RGB or intensity information of the photogrammetric point clouds, even based on a low-cost smartphone camera, contain the targeted cracks. The authors advise against using only the 3D information of the photogrammetric point clouds for crack detection due to noise. The laser triangulation system delivers the best results in both intensity and 3D information. The low weight of camera systems makes photogrammetry to the preferred method for an unmanned aerial vehicle (UAV). In the future, the authors aim for crack detection based on the 2D images, automated by using machine learning, and crack localisation by using structure from motion (SfM) or a positioning system.

Three-Dimensional Ultrasonic Crack Detection in Anisotropic Materials

1997 ◽  
Vol 9 (2) ◽  
pp. 59-79 ◽  
Author(s):  
J. Mattsson ◽  
A. J. Niklasson ◽  
A. Eriksson
Keyword(s):  
Crack Detection ◽  
Three Dimensional ◽  
Anisotropic Materials

Status of nondestructive control of transport function metal products at JSC EVRAZ NTMK

2020 ◽  
Vol 76 (6) ◽  
pp. 586-590
Author(s):  
S. P. Bersenev ◽  
E. M. Slobtsova
Keyword(s):  
Crack Detection ◽  
Middle Part ◽  
Control Line ◽  
Magnetic Powder ◽  
Nondestructive Control ◽  
Automated Control ◽  
Ultrasonic Control ◽  
Acoustic Contact ◽  
In Series

Achievements in the area of automated ultrasonic control of quality of rails, solid-rolled wheels and tyres, wheels magnetic powder crack detection, carried out at JSC EVRAZ NTMK. The 100% nondestructive control is accomplished by automated control in series at two ultrasonic facilities RWI-01 and four facilities УМКК-1 of magnetic powder control, installed into the exit control line in the wheel-tyre shop. Diagram of location, converters displacement and control operations in the process of control at the facility RWI-01 presented, as well as the structural diagram of the facility УМКК-1. The automated ultrasonic control of rough tyres is made in the tyres control line of the wheel-tyre shop at the facility УКБ-1Д. The facility enables to control internal defects of tyres in radial, axis and circular directions of radiation. Possibilities of the facility УКБ-1Д software were shown. Nondestructive control of railway rails is made at two facilities, comprising the automated control line of the rail and structural shop. The УКР-64Э facility of automated ultrasonic rails control is intended to reveal defects in the area of head, web and middle part of rail foot by pulse echo-method with a immersion acoustic contact. The diagram of rail P65 at the facility УКР-64Э control presented. To reveal defects of the macrostructure in the area of rail head and web by mirror-shadow method, an ultrasonic noncontact electromagnetic-acoustic facility is used. It was noted, that implementation of the 100% nondestructive control into the technology of rolled stuff production enabled to increase the quality of products supplied to customers and to increase their competiveness.

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