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.

Research on Bolt Crack Detection Method Based on Image Processing

2013 ◽  
Vol 433-435 ◽  
pp. 426-429
Author(s):  
Jin Qiu Liu ◽  
Bing Fa Zhang ◽  
Yu Zeng Wang ◽  
Guang Ya Li ◽  
Jing Ru Han
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Image Recognition ◽  
Crack Detection ◽  
Detection Method ◽  
Image Acquisition ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Contact Detection ◽  
Fracture Identification

A method of non-contact detection of bolt fracture have serial steps as follows: First of all the required data is obtained through image acquisition, then through the edge detection, image recognition and other image processing on the image to get the bolt fracture identification results, finally the non-contact measurement bolt fracture is realized. Experiments show that bolt crack detection method based on image processing, compared with the traditional detection methods improve the efficiency of detection and improve the detection accuracy. The method for bolt crack detection is feasible.

scholarly journals Construction of Accurate Crack Identification on Concrete Structure using Hybrid Deep Learning Approach

2021 ◽  
Vol 3 (2) ◽  
pp. 85-99
Author(s):  
Edriss Eisa Babikir Adam ◽  
Sathesh A
Keyword(s):  
Crack Detection ◽  
Concrete Bridges ◽  
Reinforced Concrete Beams ◽  
Bending Test ◽  
Random Fluctuation ◽  
Single Type ◽  
Crack Identification ◽  
Classification Error ◽  
Svm Classifier ◽  
Residual Variation

In general, several conservative techniques are available for detecting cracks in concrete bridges but they have significant limitations, including low accuracy and efficiency. Due to the expansion of the neural network method, the performance of digital image processing based crack identification has recently diminished. Many single classifier approaches are used to detect the cracks with high accuracy. The classifiers are not concentrating on random fluctuation in the training dataset and also it reflects in the final output as an over-fitting phenomenon. Though this model contains many parameters to justify the training data, it fails in the residual variation. These residual variations are frequent in UAV recorded photos as well as many camera images. To reduce this challenge, a noise reduction technique is utilized along with an SVM classifier to reduce classification error. The proposed technique is more resourceful by performing classification via SVM approach, and further the feature extraction and network training has been implemented by using the CNN method. The captured digital images are processed by incorporating the bending test through reinforced concrete beams. Moreover, the proposed method is determining the widths of the crack by employing binary conversion in the captured images. The proposed model outperforms conservative techniques, single type classifiers, and image segmentation type process methods in terms of accuracy. The obtained results have proved that, the proposed hybrid method is more accurate and suitable for crack detection in concrete bridges especially in the unmanned environment.

scholarly journals Design and Development of Street Crack Detection

2018 ◽  
Vol 7 (3.8) ◽  
pp. 82 ◽  
Author(s):  
Mr Swapnil Vilas Patil ◽  
Prof. Mangesh M. Ghonge ◽  
. .
Keyword(s):  
Image Processing ◽  
Crack Detection ◽  
Ground Truth ◽  
Automated Detection ◽  
Driving Safety ◽  
Detection Accuracy ◽  
False Detection ◽  
Safety Assurance ◽  
Ground Truth Image ◽  
Better Than

Automated detection of street cracks is a crucial project. In transportation preservation for driving safety assurance and detection a crack manually is an exceptionally tangled and time excessive method. So with the advance of science and generation, automated structures with intelligence have been accustomed examine cracks instead of people. For crack detection and characterization image processing is used widely. But because of the inhomogeneity along the cracks, the inference of noise with the same texture and complexity of cracks, image processing remain challenging. In this paper, we focused on the system performance and the additional features. System which has crack detection accuracy issue, false detection of crack issue, efficiency issue are solved in this system. For better accuracy in detecting crack and increasing the performance of the system we used the random forest algorithm. This system help to detect and characterized the crack and it find out crack from noise also i.e. it neglect the noise better than existing system. Similarly, proposed method find out the length of the crack width and depth of the crack from image with the help of ground truth image.   

Imaging-based crack detection on concrete surfaces using You Only Look Once network

2020 ◽  
pp. 147592172093848
Author(s):  
Jianghua Deng ◽  
Ye Lu ◽  
Vincent Cheng-Siong Lee
Keyword(s):  
Crack Detection ◽  
Ad Hoc ◽  
Concrete Bridges ◽  
Background Information ◽  
Detection Accuracy ◽  
Object Class ◽  
Network Training ◽  
Image Dataset ◽  
Locate Crack ◽  
Bounding Boxes

The detection of cracks in concrete structures is a pivotal aspect in assessing structural robustness. Current inspection methods are subjective, relying on the inspector’s experience and mental focus. In this study, an ad hoc You Only Look Once version 2 object detector was applied to automatically detect concrete cracks from real-world images, which were taken from diverse concrete bridges and contaminated with handwriting scripts. A total of 3010 cropped images were used to generate the dataset, labelled for two different detection classes, that is, cracks and handwriting. The proposed network was then trained and tested using the generated image dataset. Three full-scale images that contained disturbing background information were used to evaluate the robustness of the trained detector. The influence of labelling handwriting as an object class for network training on the overall crack detection accuracy was assessed as well. The results of this study show that the You Only Look Once version 2 could automatically locate crack with bounding boxes from raw images, even with the presence of handwriting scripts. As a comparative study, the proposed network was also compared with faster region-based convolutional neural network. The results showed that You Only Look Once version 2 performed better in terms of both accuracy and inference speed.

Research on Crack Detection Algorithm of Mining Car Baffle

2020 ◽  
Vol 13 ◽  
Author(s):  
Weiwei Li ◽  
Fanlei Yan
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Crack Detection ◽  
Low Cost ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Crack Identification ◽  
Actual Length ◽  
Computer Vision Technology ◽  
Sobel Edge Detection

Introduction: Image processing technology is widely used for crack detection. This technology is to build a data acquisition system and use computer vision technology for image analysis. Because of its simplicity in the processing, many of the image processing detection methods were proposed. It is relatively easy to deploy and has low cost. Method: The heterogeneity of the external light usually changes the authenticity of each target in the image, which will seriously cause the experiment to fail. At this time, the image needs to be processed by the gamma transform.Based on the analysis of the characteristics of the image of the mine car baffle, this paper improves the Gamma transform, and uses the improved Gamma transform to enhance the image. Result: We can conclude that the algorithm in this paper can accurately detect crack areas with an actual width greater than 1.2 mm, and the error between the detected crack length and the actual length is between (-2, 2) mm. In practice, this error is completely acceptable. Discussion: To compare the performance of a new crack detection method with existing methods, are used. The two most well-known traditional methods, Canny and Sobel edge detection, are selected. Although the Sobel edge detection provides some crack information. The texture of the surface of the mine cart baffle detected has caused great interference to the crack identification. Conclusion: If the cracks appearing on the mine car baffle are not found in time, they often cause accidents. Therefore, effective crack detection must be performed. If manual inspection is adopted for crack detection, it will be labor-intensive and easy to miss inspection. In order to reduce the labor of crack detection of mine cars and improve the accuracy of detection, this paper, based on the detection platform built, performs preprocessing, image enhancement, and convolution operations on the collected crack images of the mine car baffle.

scholarly journals Pixel-Wise Crack Detection Using Deep Local Pattern Predictor for Robot Application

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3042 ◽  
Author(s):  
Yundong Li ◽  
Hongguang Li ◽  
Hongren Wang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Visual Inspection ◽  
Crack Detection ◽  
Concrete Surface ◽  
Concrete Bridges ◽  
Experimental Results ◽  
Inspection Method ◽  
Local Pattern ◽  
Robotic Vision

Robotic vision-based crack detection in concrete bridges is an essential task to preserve these assets and their safety. The conventional human visual inspection method is time consuming and cost inefficient. In this paper, we propose a robust algorithm to detect cracks in a pixel-wise manner from real concrete surface images. In practice, crack detection remains challenging in the following aspects: (1) detection performance is disturbed by noises and clutters of environment; and (2) the requirement of high pixel-wise accuracy is difficult to obtain. To address these limitations, three steps are considered in the proposed scheme. First, a local pattern predictor (LPP) is constructed using convolutional neural networks (CNN), which can extract discriminative features of images. Second, each pixel is efficiently classified into crack categories or non-crack categories by LPP, using as context a patch centered on the pixel. Lastly, the output of CNN—i.e., confidence map—is post-processed to obtain the crack areas. We evaluate the proposed algorithm on samples captured from several concrete bridges. The experimental results demonstrate the good performance of the proposed method.

scholarly journals Concrete Crack Detection Algorithm Based on Deep Residual Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xiuying Meng
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Deep Learning ◽  
Crack Detection ◽  
Learning Algorithm ◽  
Detection Algorithm ◽  
Concrete Pavement ◽  
Computer Hardware ◽  
Detection Accuracy ◽  
Generalization Ability

Crack is the early expression form of the concrete pavement disease. Early discovery and treatment of it can play an important role in the maintenance of the pavement. With ongoing advancements in computer hardware technology, continual optimization of deep learning algorithms, as compared to standard digital image processing algorithms, utilizing automation of crack detection technology has a deep learning algorithm that is more exact. As a result of the benefits of greater robustness, the study of concrete pavement crack picture has become popular. In view of the poor effect and weak generalization ability of traditional image processing technology on image segmentation of concrete cracks, this paper studies the image segmentation algorithm of concrete cracks based on convolutional neural network and designs an end-to-end segmentation model based on ResNet101. It integrates more low-level features, which make the fracture segmentation results more refined and closer to the practical application scenarios. Compared with other methods, the algorithm in this paper has achieved higher detection accuracy and generalization ability.

Crack identification method for concrete structures considering angle of view using RGB-D camera-based sensor fusion

2020 ◽  
pp. 147592172093475
Author(s):  
Hyunjun Kim ◽  
Sahyeon Lee ◽  
Eunjong Ahn ◽  
Myoungsu Shin ◽  
Sung-Han Sim
Keyword(s):  
High Resolution ◽  
Sensor Fusion ◽  
Crack Width ◽  
Measurement Accuracy ◽  
Concrete Structures ◽  
Digital Camera ◽  
Concrete Surface ◽  
Structural Safety ◽  
Crack Identification ◽  
Angle Of View

Cracks on concrete structures are an important indicator for assessing concrete durability and structural safety. Although such cracks are typically monitored by manual visual inspection, this method has drawbacks in terms of inspection time, safety, cost-effectiveness, and measurement accuracy. An innovative alternative is digital image processing, which can be used to obtain crack information from images captured using a digital camera. However, in image-based crack detection, the crack width may vary depending on the angle of the camera with respect to the concrete surface. A skewed angle of view is often encountered, particularly when capturing images from unmanned aerial vehicles or from higher locations. This study proposes a crack identification strategy using a combination of RGB-D and high-resolution digital cameras to accurately measure cracks regardless of the angle of view. The camera system is equipped with a tailored sensor fusion algorithm for crack identification, enabling a high measurement resolution and a robust depth estimation considering the skewed angle problem. An approximate plane corresponding to the concrete surface is introduced to effectively handle the high noise in the depth measurement data of the RGB-D camera. Subsequently, the crack image captured using the high-resolution digital camera is mapped onto the obtained plane model, allowing the crack width to be determined using the three-dimensional coordinates of each crack pixel. The measurement accuracy of the proposed approach is experimentally validated on an actual concrete structure.

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.

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