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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

CrackGAN: Pavement Crack Detection Using Partially Accurate Ground Truths Based on Generative Adversarial Learning
Kaige Zhang, Yingtao Zhang, Heng-Da Cheng
IEEE Transactions on Intelligent Transportation Systems (2020) Vol. 22, Iss. 2, pp. 1306-1319
Open Access | Times Cited: 154

Showing 1-25 of 154 citing articles:

Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning
Dimitrios Dais, İhsan Engin Bal, Eleni Smyrou, et al.
Automation in Construction (2021) Vol. 125, pp. 103606-103606
Open Access | Times Cited: 307
GAN-based anomaly detection: A review
Xuan Xia, Xizhou Pan, Nan Li, et al.
Neurocomputing (2022) Vol. 493, pp. 497-535
Closed Access | Times Cited: 234
Computer vision framework for crack detection of civil infrastructure—A review
Dihao Ai, Guiyuan Jiang, Siew-Kei Lam, et al.
Engineering Applications of Artificial Intelligence (2022) Vol. 117, pp. 105478-105478
Closed Access | Times Cited: 151
Road Condition Monitoring Using Smart Sensing and Artificial Intelligence: A Review
Eshta Ranyal, Ayan Sadhu, Kamal Jain
Sensors (2022) Vol. 22, Iss. 8, pp. 3044-3044
Open Access | Times Cited: 111
Automatic Detection and Counting System for Pavement Cracks Based on PCGAN and YOLO-MF
Duo Ma, Hongyuan Fang, Niannian Wang, et al.
IEEE Transactions on Intelligent Transportation Systems (2022) Vol. 23, Iss. 11, pp. 22166-22178
Closed Access | Times Cited: 99
A Comprehensive Review of Deep Learning-Based Crack Detection Approaches
Younes Hamishebahar, Hong Guan, Stephen So, et al.
Applied Sciences (2022) Vol. 12, Iss. 3, pp. 1374-1374
Open Access | Times Cited: 93
A deeper generative adversarial network for grooved cement concrete pavement crack detection
Jingtao Zhong, Ju Huyan, Weiguang Zhang, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 119, pp. 105808-105808
Closed Access | Times Cited: 51
Surface concrete cracks detection and segmentation using transfer learning and multi-resolution image processing
Mostafa Iraniparast, Sajad Ranjbar, Mohammad Rahai, et al.
Structures (2023) Vol. 54, pp. 386-398
Closed Access | Times Cited: 47
Unmanned Aerial Vehicle (UAV)-Based Pavement Image Stitching Without Occlusion, Crack Semantic Segmentation, and Quantification
Jinhuan Shan, Wei Jiang, Yue Huang, et al.
IEEE Transactions on Intelligent Transportation Systems (2024) Vol. 25, Iss. 11, pp. 17038-17053
Closed Access | Times Cited: 19
Deep learning for surface crack detection in civil engineering: A comprehensive review
Haiyan Zhuang, Yikai Cheng, Man Zhou, et al.
Measurement (2025), pp. 116908-116908
Closed Access | Times Cited: 2
Machine vision-based surface crack analysis for transportation infrastructure
Wenbo Hu, Weidong Wang, Chengbo Ai, et al.
Automation in Construction (2021) Vol. 132, pp. 103973-103973
Closed Access | Times Cited: 82
Defect detection and quantification in electroluminescence images of solar PV modules using U-net semantic segmentation
Lawrence E Pratt, Devashen Govender, Richard Klein
Renewable Energy (2021) Vol. 178, pp. 1211-1222
Closed Access | Times Cited: 68
Deep learning-based crack segmentation for civil infrastructure: data types, architectures, and benchmarked performance
Shanglian Zhou, Carlos Canchila, Wei Song
Automation in Construction (2022) Vol. 146, pp. 104678-104678
Closed Access | Times Cited: 62
Automatic pixel-level crack segmentation in images using fully convolutional neural network based on residual blocks and pixel local weights
Ali Raza, Joon Huang Chuah, Mohamad Sofian Abu Talip, et al.
Engineering Applications of Artificial Intelligence (2021) Vol. 104, pp. 104391-104391
Closed Access | Times Cited: 58
A review of the research and application of deep learning-based computer vision in structural damage detection
Lingxin Zhang, Junkai Shen, Zhu Baijie
Earthquake Engineering and Engineering Vibration (2022) Vol. 21, Iss. 1, pp. 1-21
Closed Access | Times Cited: 58
Deep Learning-Based Crack Detection: A Survey
Son Dong Nguyen, Thai Son Tran, Van Phuc Tran, et al.
International Journal of Pavement Research and Technology (2022) Vol. 16, Iss. 4, pp. 943-967
Closed Access | Times Cited: 50
Recent computer vision applications for pavement distress and condition assessment
Ayman H. El Hakea, Mohamed Waleed Fakhr
Automation in Construction (2022) Vol. 146, pp. 104664-104664
Closed Access | Times Cited: 40
A sigmoid‐optimized encoder–decoder network for crack segmentation with copy‐edit‐paste transfer learning
Firdes Çelik, Markus König
Computer-Aided Civil and Infrastructure Engineering (2022) Vol. 37, Iss. 14, pp. 1875-1890
Open Access | Times Cited: 39
How Generative Adversarial Networks Promote the Development of Intelligent Transportation Systems: A Survey
Hongyi Lin, Yang Liu, Shen Li, et al.
IEEE/CAA Journal of Automatica Sinica (2023) Vol. 10, Iss. 9, pp. 1781-1796
Closed Access | Times Cited: 32
Establishment and evaluation of conditional GAN-based image dataset for semantic segmentation of structural cracks
Tao Jin, Xiao‐Wei Ye, Zecheng Li
Engineering Structures (2023) Vol. 285, pp. 116058-116058
Closed Access | Times Cited: 28
A Novel Data Augmentation Method for Improved Visual Crack Detection Using Generative Adversarial Networks
Efstathios Branikas, Paul Murray, Graeme West
IEEE Access (2023) Vol. 11, pp. 22051-22059
Open Access | Times Cited: 24
Pavement Cracks Coupled With Shadows: A New Shadow-Crack Dataset and A Shadow-Removal-Oriented Crack Detection Approach
Lili Fan, Shen Li, Ying Li, et al.
IEEE/CAA Journal of Automatica Sinica (2023) Vol. 10, Iss. 7, pp. 1593-1607
Closed Access | Times Cited: 24
DeepCrackAT: An effective crack segmentation framework based on learning multi-scale crack features
Qinghua Lin, Wei Li, Xiangpan Zheng, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 126, pp. 106876-106876
Closed Access | Times Cited: 24
Multi-stage generative adversarial networks for generating pavement crack images
Chengjia Han, Tao Ma, Ju Huyan, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 131, pp. 107767-107767
Closed Access | Times Cited: 14
Generative adversarial networks in construction applications
Chai Ping, Lei Hou, Guomin Zhang, et al.
Automation in Construction (2024) Vol. 159, pp. 105265-105265
Open Access | Times Cited: 13
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