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.

Image-Based Crack Detection Methods: A Review

A triple-thresholds pavement crack detection method leveraging random structured forest

Machine vision-based surface crack analysis for transportation infrastructure

Automated crack severity level detection and classification for ballastless track slab using deep convolutional neural network

Pixel-level crack detection is crucial in pavement performance assessment. Current deep learning-based detection methods first encode input images by multi-scale feature maps, then decode them to t...

Pixel-level pavement crack detection using enhanced high-resolution semantic network

A small amount of research has been conducted in dealing with pavement shadow–affected cracks detection. Hence, this research aims to develop the illumination compensation model (ICM) and k...

Shuai Yan

Papers

Illumination Compensation Model with k -Means Algorithm for Detection of Pavement Surface Cracks with Shadow