‘Statistical methods for automatic crack detection based on vibrothermography sequence-of-image data’ by M. Li, S. D. Holland and W. Q. Meeker: Discussion 2

2010 ◽  
Vol 26 (5) ◽  
pp. 502-508
Author(s):  
Guérin Fabrice
Keyword(s):  
Statistical Methods ◽  
Crack Detection ◽  
Image Data

scholarly journals UAV-Driven Structural Crack Detection and Location Determination Using Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2650
Author(s):  
Daegyun Choi ◽  
William Bell ◽  
Donghoon Kim ◽  
Jichul Kim
Keyword(s):  
Visual Information ◽  
Crack Detection ◽  
Image Data ◽  
Image Processing Techniques ◽  
Location Determination ◽  
Aerial Vehicle ◽  
Using Data ◽  
Processing Techniques ◽  
Deep Learning Model ◽  
Structural Cracks

Structural cracks are a vital feature in evaluating the health of aging structures. Inspectors regularly monitor structures’ health using visual information because early detection of cracks on highly trafficked structures is critical for maintaining the public’s safety. In this work, a framework for detecting cracks along with their locations is proposed. Image data provided by an unmanned aerial vehicle (UAV) is stitched using image processing techniques to overcome limitations in the resolution of cameras. This stitched image is analyzed to identify cracks using a deep learning model that makes judgements regarding the presence of cracks in the image. Moreover, cracks’ locations are determined using data from UAV sensors. To validate the system, cracks forming on an actual building are captured by a UAV, and these images are analyzed to detect and locate cracks. The proposed framework is proven as an effective way to detect cracks and to represent the cracks’ locations.

scholarly journals Vision Measurement of Tunnel Structures with Robust Modelling and Deep Learning Algorithms

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4945 ◽  
Author(s):  
Xiangyang Xu ◽  
Hao Yang
Keyword(s):  
Deep Learning ◽  
Health Monitoring ◽  
Crack Detection ◽  
Three Dimensional ◽  
Image Data ◽  
Measurement Unit ◽  
Crack Identification ◽  
Monitoring Method ◽  
Vision Measurement ◽  
Robust Modelling

The health monitoring of tunnel structures is vital to the safe operation of railway transportation systems. With the increasing mileage of tunnels, regular inspection and health monitoring are urgently demanded for the tunnel structures, especially for information regarding deformation and damage. However, traditional methods of tunnel inspection are time-consuming, expensive and highly dependent on human subjectivity. In this paper, an automatic tunnel monitoring method is investigated based on image data which is collected through the moving vision measurement unit consisting of camera array. Furthermore, geometric modelling and crack inspection algorithms are proposed where a robust three-dimensional tunnel model is reconstructed utilizing a B-spline method and crack identification is conducted by means of a Mask R-CNN network. The innovation of this investigation is that we combine the robust modelling which could be applied for the deformation analysis and the crack detection where a deep learning method is employed to recognize the tunnel cracks intelligently based on image sensors. In this study, experiments were conducted on a subway tunnel structure several kilometers long, and a robust three-dimensional model is generated and the cracks are identified automatically with the image data. The superiority of this proposal is that the comprehensive information of geometry deformation and crack damage can ensure the reliability and improve the accuracy of health monitoring.

Deep-learning-based crack detection with applications for the structural health monitoring of gas turbines

2019 ◽  
Vol 19 (5) ◽  
pp. 1440-1452 ◽  
Author(s):  
Mahtab Mohtasham Khani ◽  
Sahand Vahidnia ◽  
Leila Ghasemzadeh ◽  
Y Eren Ozturk ◽  
Mustafa Yuvalaklioglu ◽  
...  
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Gas Turbines ◽  
Crack Detection ◽  
Image Data ◽  
Classification Performance ◽  
Data Set ◽  
Structural Health ◽  
Repair Costs ◽  
Classical Image

Gas turbine maintenance requires consistent inspections of cracks and other structural anomalies. The inspections provide information regarding the overall condition of the structures and yield information for estimating structural health and repair costs. Various image processing techniques have been used in the past to address the problem of automated visual crack detection with varying degrees of success. In this work, we propose a novel crack detection framework that utilizes techniques from both classical image processing and deep learning methodologies. The main contribution of this work is demonstrating that applying filters to image data in the pre-processing phase can significantly boost the classification performance of a convolutional neural network–based model. The developed architecture outperforms compared works by yielding a 96.26% classification accuracy on a data set of cracked surface images collected from gas turbines.

Automatic Detection of the Cracks on the Concrete Railway Sleepers

2019 ◽  
Vol 33 (09) ◽  
pp. 1955010
Author(s):  
Seyed Amir Hossein Tabatabaei ◽  
Ahmad Delforouzi ◽  
Muhammad Hassan Khan ◽  
Tim Wesener ◽  
Marcin Grzegorzek
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Intelligent Systems ◽  
Crack Detection ◽  
Image Data ◽  
Experimental Comparison ◽  
Pattern Recognition Techniques ◽  
Comparison Results ◽  
Recognition Systems ◽  
Computer Recognition

A vision-based method for detecting the cracks in the concrete sleepers of the railway tracks will be introduced in this paper. The method is able to detect and partially classify the cracks of the concrete sleepers in two successive steps based on the image processing and pattern recognition techniques. The method has been implemented on the acquired image data frames followed by the analysis, experimental, comparison results and evaluation. The presented results are reasonable which indicates the goodness of the introduced method. The preliminary results of this work have been presented in [A. Delforouzi, A. H. Tabatabaei, M. H. Khan and M. Grzegorzek, A vision-based method for automatic crack detection in railway sleepers, in Kurzynski, M., Wozniak, M., Burduk, R. (eds.), Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017, Polanica Zdroj, Poland. CORES 2017. Advances in Intelligent Systems and Computing, Vol. 578 (Springer, Cham, 2018), pp. 130–139, doi: 10.1007/978-3-319-59162-9_14].

scholarly journals Pavement Crack Detection Using Convolutional Neural Network

10.29007/h4k6 ◽  
2020 ◽  
Author(s):  
Alaa Sheta ◽  
Hamza Turabieh ◽  
Sultan Aljahdali ◽  
Abdulaziz Alangari
Keyword(s):  
Neural Network ◽  
New York ◽  
Convolutional Neural Network ◽  
Crack Detection ◽  
Stable Condition ◽  
Image Data ◽  
Data Sets ◽  
Data Set ◽  
Data Division ◽  
The Usa

Automating the process of detecting pavement cracks became a challenge mission. In the last few decades, many methods were proposed to solve this problem. The reason is that maintaining a stable condition of roads is essential for the safety of people and public properties. It was reported that maintaining one mile of roads in New York City in the USA might cost from four to ten thousand dollars. In this paper, we explore our initial idea of developing a lightweight Convolutional Neural Network (CNN or ConvNet) model that can be used to detect pavement cracks. The proposed CNN was trained using the AigleRN data set, which contains 400 images of road cracks of 480×320 resolution. The proposed lightweight CNN architecture performed a better fitting to the image data set due to the reduction in the number of parameters. The proposed CNN was capable of detecting cracks with a various number of sample images. We simulated the CNN architecture over different sizes of training/testing (i.e., 90/10, 80/20, and 70/30) data sets for 11 runs. The obtained results show that 90/10 data division for training and testing is outperformed other categories with an average accuracy of 97.27%.

A Ceramic Crack Test Method Based on the Maximum Variance Ratio of Inter-Class and Intra-Class

2014 ◽  
Vol 633-634 ◽  
pp. 372-376
Author(s):  
Bing Xiang Liu ◽  
Feng Qin Wang ◽  
Cheng Le Yu
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Object Recognition ◽  
Crack Detection ◽  
Detection Threshold ◽  
Image Data ◽  
Test Method ◽  
Detection Methods ◽  
Variance Ratio ◽  
Maximum Variance

This paper presents a method for inter-class ceramic crack detection threshold than the maximum variance within the class-based segmentation. Ceramics crack detection methods are mainly obtained by ceramic image data, image pre-processing, image segmentation, feature extraction and object recognition constitutes five links. Experimental results show that the method can be detected quickly and accurately detect whether the standard ceramic.

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