TY - JOUR
T1 - Bibliometric Analysis and Review of Deep Learning-Based Crack Detection Literature Published between 2010 and 2022
AU - Ali, Luqman
AU - Alnajjar, Fady
AU - Khan, Wasif
AU - Serhani, Mohamed Adel
AU - Al Jassmi, Hamad
N1 - Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/4
Y1 - 2022/4
N2 - The use of deep learning (DL) in civil inspection, especially in crack detection, has in-creased 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 suitabil-ity, (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.
AB - The use of deep learning (DL) in civil inspection, especially in crack detection, has in-creased 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 suitabil-ity, (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.
KW - bibliometric analysis
KW - civil inspection
KW - crack detection
KW - deep learning
KW - literature review
KW - vision-based inspection
UR - http://www.scopus.com/inward/record.url?scp=85128300575&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85128300575&partnerID=8YFLogxK
U2 - 10.3390/buildings12040432
DO - 10.3390/buildings12040432
M3 - Review article
AN - SCOPUS:85128300575
SN - 2075-5309
VL - 12
JO - Buildings
JF - Buildings
IS - 4
M1 - 432
ER -