Bibliometric Analysis and Review of Deep Learning-Based Crack Detection Literature Published between 2010 and 2022

Abstract

The use of deep learning (DL) in civil inspection, especially in crack detection, has increased over the past years to ensure long-term structural safety and integrity. To achieve a better understanding of the research work on crack detection using DL approaches, this paper aims to provide a bibliometric analysis and review of the current literature on DL-based crack detection published between 2010 and 2022. The search from Web of Science (WoS) and Scopus, two widely accepted bibliographic databases, resulted in 165 articles published in top journals and conferences, showing the rapid increase in publications in this area since 2018. The evolution and state-of-the-art approaches to crack detection using deep learning are reviewed and analyzed based on datasets, network architecture, domain, and performance of each study. Overall, this review article stands as a reference for researchers working in the field of crack detection using deep learning techniques to achieve optimal precision and computational efficiency performance in light of electing the most effective combination of dataset characteristics and network architecture for each domain. Finally, the challenges, gaps, and future directions are provided to researchers to explore various solutions pertaining to (a) automatic recognition of crack type and severity, (b) dataset availability and suitability, (c) efficient data preprocessing techniques, (d) automatic labeling approaches for crack detection, (e) parameter tuning and optimization, (f) using 3D images and data fusion, (g) real-time crack detection, and (h) increasing segmentation accuracy at the pixel level.

Bibliometric analysis of the published literature on machine learning in economics and econometrics

“…Today, despite the existence of numerous studies in different fields that use big data and machine learning algorithms it is observed that there are a few studies that examine big data and machine learning through bibliometric analysis. Among these studies, for instance, while Belmonte et al ( 2020 ) examined big data and machine learning simultaneously in their bibliometric analysis, Alonso et al ( 2018 ), Dhamija and Bag ( 2020 ), Tran et al ( 2019 ) focused on artificial intelligence, and Li et al ( 2020 ), Ali et al ( 2022 ), Bidwe et al ( 2022 ), Mao et al ( 2018 ), Nakhodchi and Dehghantanha ( 2020 ) focused on deep learning. Many researchers, including Mishra et al ( 2018 ); Xian and Madhavan ( 2014 ); Liao et al ( 2018 ); Ardito et al ( 2019 ); Liu et al ( 2019 ) and Rialti et al ( 2019 ), Kalantari et al ( 2017 ) have reviewed studies concerning big data.…”

SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D data

“…The problem of road damage detection using image-based techniques has gained great importance in the last 15 years with the explosion of Computer Vision and Pattern Recognition methods. This rapid growth has led to the publication of numerous surveys comparing different methods, such as [27,49,2]. The proposed methods vary in terms of the type of data analyzed and the approach.…”

Automated Detection for Concrete Surface Cracks Based on Deeplabv3+ BDF

“…Zhao et al [9] proposed a crack feature pyramid network (Crack-FPN), which has superior feature extraction capability and reduced computational cost. Some research or reviews on the application of objection detection algorithms to crack detection have also been carried out [10][11][12][13][14][15][16]. However, due to the simplicity of the result form, the target detection algorithm is only applicable to simple target existence determination.…”