Data-driven approach for AI-based crack detection: techniques, challenges, and future scope

Abstract

This article provides a systematic literature review on the application of artificial intelligence (AI) technology for detecting cracks in civil infrastructure, which is a critical issue affecting the performance and longevity of these structures. Traditional crack detection methods involve manual inspection, which is laborious and time-consuming, especially in urban areas. Therefore, automatic crack detection with AI technology has gained popularity due to its ability to identify degradation of roads in real-time, leading to increased safety and reliability. This review emphasizes two key approaches for crack detection: deep learning and traditional computer vision, with a focus on data-driven aspects that rely primarily on data from training datasets to detect and quantify the severity level of the crack. The article highlights the advantages and drawbacks of each approach and provides an overview of various crack detection models, feature extraction techniques, datasets, potential issues, and future directions. The research concludes that deep learning-based methods used for crack classification, localization and segmentation have shown better performance than traditional computer vision techniques, especially in terms of accuracy. However, deep learning methods require large amounts of training data and computational power, which can be a significant limitation. Additionally, the article identifies a lack of 3D datasets, unsupervised learning algorithms are rarely used to train crack detection model, and datasets having road images with variety of road textures such as asphalt and cement etc. as challenges for future research in this field. A need for 3D and combined texture datasets as challenges for future research in this field.

An Integrated Framework for Image Acquisition, Processing, and Analysis Procedures for Automated Damage Evaluation of Concrete Surfaces

“…Nevertheless, there is a shortage of datasets containing the required images to train machine learning models for quantifying the extent of cracking (i.e., minimal annotated datasets) [16,17]. Moreover, most of the literature is focused on the crack detection task [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] where images are To perform a quantitative analysis of the damage extent to monitor volumetric expansion over time, researchers have introduced a surface crack mapping technique known as the cracking index (CI). This non-destructive quantitative tool assesses the degree of damage, estimating concrete expansion by measuring the widths of cracks observed on the ASR-affected concrete surface [6,7].…”

“…Nevertheless, there is a shortage of datasets containing the required images to train machine learning models for quantifying the extent of cracking (i.e., minimal annotated datasets) [16,17]. Moreover, most of the literature is focused on the crack detection task [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] where images are taken near the surface and without quantification of the damage. In a condition assessment, a quantitative value is required to inform the decision regarding the next steps and to monitor the increase in the damage over time to capture the rate of the damage.…”