Crack detection using image processing: A critical review and analysis
Arun Mohan,Sumathi Poobal +1 more
TLDR
In this paper, a detailed survey is conducted to identify the research challenges and the achievements till in this field, and those research papers are reviewed based on the image processing techniques, objectives, accuracy level, error level, and the image data sets.Abstract:
Cracks on the concrete surface are one of the earliest indications of degradation of the structure which is critical for the maintenance as well the continuous exposure will lead to the severe damage to the environment. Manual inspection is the acclaimed method for the crack inspection. In the manual inspection, the sketch of the crack is prepared manually, and the conditions of the irregularities are noted. Since the manual approach completely depends on the specialist’s knowledge and experience, it lacks objectivity in the quantitative analysis. So, automatic image-based crack detection is proposed as a replacement. Literature presents different techniques to automatically identify the crack and its depth using image processing techniques. In this research, a detailed survey is conducted to identify the research challenges and the achievements till in this field. Accordingly, 50 research papers are taken related to crack detection, and those research papers are reviewed. Based on the review, analysis is provided based on the image processing techniques, objectives, accuracy level, error level, and the image data sets. Finally, we present the various research issues which can be useful for the researchers to accomplish further research on the crack detection.read more
Citations
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Journal ArticleDOI
Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete
TL;DR: Computational times for DCNN are shorter than the most efficient edge detection algorithms, not considering the training process, and show significant promise for future adoption of DCNN methods for image-based damage detection in concrete.
Journal ArticleDOI
Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone.
TL;DR: For the first time, a large-scale road damage dataset is prepared and it is demonstrated that the type of damage can be classified into eight types with high accuracy by applying the proposed object detection method.
Journal ArticleDOI
Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review.
TL;DR: The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed.
Journal ArticleDOI
Image-based concrete crack detection in tunnels using deep fully convolutional networks
TL;DR: An improved deep fully convolutional neural network, named as CrackSegNet, is proposed to conduct dense pixel-wise crack segmentation, making tunnel inspection and monitoring highly efficient, low cost, and eventually automatable.
Journal ArticleDOI
A systematic review of convolutional neural network-based structural condition assessment techniques
TL;DR: A detailed literature review of existing CNN-based techniques in the context of infrastructure monitoring and maintenance and a brief conclusion on potential future research directions of CNN in structural condition assessment is presented.
References
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TL;DR: In this paper, a self-consistent method for the elastic moduli of bodies containing randomly distributed flat cracks, with or without fluid in their interiors, is proposed and general concepts are outlined for arbitrary cracks and explicit derivations together with numerical results are given.
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TL;DR: The proposed CrackTree method is evaluated on a collection of 206 real pavement images and the experimental results show that the proposed method achieves a better performance than several existing methods.
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TL;DR: A fully integrated system for the automatic detection and characterization of cracks in road flexible pavement surfaces, which does not require manually labeled samples, is proposed to minimize the human subjectivity resulting from traditional visual surveys.
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Fast crack detection method for large-size concrete surface images using percolation-based image processing
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Image-based retrieval of concrete crack properties for bridge inspection
TL;DR: In this paper, an integrated model consisting of crack quantification, change detection, neural networks, and 3D visualization models to visualize the defects in such a way that it mimics the on-site visual inspections is presented.