Crack detection using image processing: A critical review and analysis

By A. Mohan, S. Poobal. Alexandria Engineering Journal, 2017

882 citations recorded by the source corpus.

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.

Subfields: Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/4fee69cf3608caaf093c07ce998dc646714ffed1

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In Civil and Structural Engineering

Citations in the corpus

Automated crack detection and measurement based on digital image correlation (2020, methodology): “Such procedures aim to gain more objectivity in the quantitative analysis compared to the visual crack identification process by eye, which is tedious and depends heavily on the specialist’s knowledge and experience [12].”
Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images (2018, background): “(Mohan and Poobal, 2017), there are few studies that construct damage detection models using real data, and 20 of these studies use road images taken directly from above the road.”
A systematic review of convolutional neural network-based structural condition assessment techniques (2021, methodology): “Recently, Mohan and Poobal 78 (2018) reviewed various image processing techniques for detecting cracks in concrete surfaces and 79 concluded that the direction of the crack was crucial to the ability to detect and quantify the size 80 of cracks.”
Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete (2018, background): “[44] A. Mohan and S. Poobal, "Crack detection using image processing: A critical review and analysis," Alexandria Engineering Journal, vol.”