Image-Based Crack Detection Methods: A Review

Hafiz Suliman Munawar,Carlos Alberto Pereira Soares,Ahmed W A Hammad,S Travis Waller,Assed Haddad

doi:10.3390/infrastructures6080115

Hafiz Suliman Munawar, Carlos Alberto Pereira Soares + Show 3 more

Open Access

https://doi.org/10.3390/infrastructures6080115

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Abstract

Annually, millions of dollars are spent to carry out defect detection in key infrastructure including roads, bridges, and buildings. The aftermath of natural disasters like floods and earthquakes leads to severe damage to the urban infrastructure. Maintenance operations that follow for the damaged infrastructure often involve a visual inspection and assessment of their state to ensure their functional and physical integrity. Such damage may appear in the form of minor or major cracks, which gradually spread, leading to ultimate collapse or destruction of the structure. Crack detection is a very laborious task if performed via manual visual inspection. Many infrastructure elements need to be checked regularly and it is therefore not feasible as it will require significant human resources. This may also result in cases where cracks go undetected. A need, therefore, exists for performing automatic defect detection in infrastructure to ensure its effectiveness and reliability. Using image processing techniques, the captured or scanned images of the infrastructure parts can be analyzed to identify any possible defects. Apart from image processing, machine learning methods are being increasingly applied to ensure better performance outcomes and robustness in crack detection. This paper provides a review of image-based crack detection techniques which implement image processing and/or machine learning. A total of 30 research articles have been collected for the review which is published in top tier journals and conferences in the past decade. A comprehensive analysis and comparison of these methods are performed to highlight the most promising automated approaches for crack detection.

Highlights

Millions of dollars are spent to acquire various tools and assets to carry out defect detection from key infrastructure which includes roads, bridges, buildings, and water bodies [1]
Civil structures such as roads, bridges, buildings, and pavements are often exposed to extreme physical stress which may be caused by natural disasters like earthquakes, catastrophic incidents like blasts or daily usage
A total of 20 videos were captured from components in a nuclear power plant and integrated Convolutional Neural Network (CNN) and a naïve Bayesian (NB) classifier to fuse and analyze the data acquired from each video frame

Summary

Introduction

Millions of dollars are spent to acquire various tools and assets to carry out defect detection from key infrastructure which includes roads, bridges, buildings, and water bodies [1] Civil structures such as roads, bridges, buildings, and pavements are often exposed to extreme physical stress which may be caused by natural disasters like earthquakes, catastrophic incidents like blasts or daily usage. Cracks emerge at a microscopic level on the surface of the infrastructure component [2] These cracks make the component weak, reduce its loading capacity and lead to discontinuities on the surface [3,4,5]. Manual methods of crack detection involve experts who examine the component visually and the use of specific

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