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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:

Pavement crack detection and recognition using the architecture of segNet
Tingyang Chen, Zhenhua Cai, Xi Zhao, et al.
Journal of Industrial Information Integration (2020) Vol. 18, pp. 100144-100144
Closed Access | Times Cited: 133

Showing 1-25 of 133 citing articles:

Structural crack detection using deep convolutional neural networks
Ali Raza, Joon Huang Chuah, Mohamad Sofian Abu Talip, et al.
Automation in Construction (2021) Vol. 133, pp. 103989-103989
Closed Access | Times Cited: 233
Vision transformer-based autonomous crack detection on asphalt and concrete surfaces
Elyas Asadi Shamsabadi, Chang Xu, Aravinda S. Rao, et al.
Automation in Construction (2022) Vol. 140, pp. 104316-104316
Closed Access | Times Cited: 128
Pavement crack detection based on transformer network
Feng Guo, Yu Qian, Jian Liu, et al.
Automation in Construction (2022) Vol. 145, pp. 104646-104646
Closed Access | Times Cited: 126
Machine learning techniques for pavement condition evaluation
Nima Sholevar, Amir Golroo, Sahand Roghani Esfahani
Automation in Construction (2022) Vol. 136, pp. 104190-104190
Closed Access | Times Cited: 122
Real-time detection of cracks in tiled sidewalks using YOLO-based method applied to unmanned aerial vehicle (UAV) images
Qiwen Qiu, Denvid Lau
Automation in Construction (2023) Vol. 147, pp. 104745-104745
Closed Access | Times Cited: 113
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: 92
Crack detection algorithm for concrete structures based on super-resolution reconstruction and segmentation network
Chaoqun Xiang, Wei Wang, Lu Deng, et al.
Automation in Construction (2022) Vol. 140, pp. 104346-104346
Closed Access | Times Cited: 75
Defect transformer: An efficient hybrid transformer architecture for surface defect detection
Junpu Wang, Guili Xu, Fuju Yan, et al.
Measurement (2023) Vol. 211, pp. 112614-112614
Open Access | Times Cited: 56
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: 46
A lightweight encoder–decoder network for automatic pavement crack detection
Guijie Zhu, Jiacheng Liu, Zhun Fan, et al.
Computer-Aided Civil and Infrastructure Engineering (2023) Vol. 39, Iss. 12, pp. 1743-1765
Open Access | Times Cited: 42
Smart and Automated Infrastructure Management: A Deep Learning Approach for Crack Detection in Bridge Images
Hina Inam, Naeem Ul Islam, Muhammad Usman Akram, et al.
Sustainability (2023) Vol. 15, Iss. 3, pp. 1866-1866
Open Access | Times Cited: 40
Research and applications of artificial neural network in pavement engineering: A state-of-the-art review
Xu Yang, Jinchao Guan, Ling Ding, et al.
Journal of Traffic and Transportation Engineering (English Edition) (2021) Vol. 8, Iss. 6, pp. 1000-1021
Open Access | Times Cited: 86
Dual attention deep learning network for automatic steel surface defect segmentation
Yue Pan, L. Zhang
Computer-Aided Civil and Infrastructure Engineering (2021) Vol. 37, Iss. 11, pp. 1468-1487
Closed Access | Times Cited: 79
CNN Based on Transfer Learning Models Using Data Augmentation and Transformation for Detection of Concrete Crack
Md. Monirul Islam, Md. Belal Hossain, Md. Nasim Akhtar, et al.
Algorithms (2022) Vol. 15, Iss. 8, pp. 287-287
Open Access | Times Cited: 66
An ensemble learning model for asphalt pavement performance prediction based on gradient boosting decision tree
Runhua Guo, Donglei Fu, Giuseppe Sollazzo
International Journal of Pavement Engineering (2021) Vol. 23, Iss. 10, pp. 3633-3646
Closed Access | Times Cited: 65
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: 60
Unifying transformer and convolution for dam crack detection
Erhu Zhang, Linhao Shao, Yang Wang
Automation in Construction (2022) Vol. 147, pp. 104712-104712
Closed Access | Times Cited: 43
Modeling automatic pavement crack object detection and pixel-level segmentation
Yuchuan Du, Shan Zhong, Hongyuan Fang, et al.
Automation in Construction (2023) Vol. 150, pp. 104840-104840
Closed Access | Times Cited: 37
ECSNet: An Accelerated Real-Time Image Segmentation CNN Architecture for Pavement Crack Detection
Tianjie Zhang, Donglei Wang, Yang Lu
IEEE Transactions on Intelligent Transportation Systems (2023) Vol. 24, Iss. 12, pp. 15105-15112
Closed Access | Times Cited: 31
Image Processing Techniques for Concrete Crack Detection: A Scientometrics Literature Review
Md. Al-Masrur Khan, Seong‐Hoon Kee, Al‐Sakib Khan Pathan, et al.
Remote Sensing (2023) Vol. 15, Iss. 9, pp. 2400-2400
Open Access | Times Cited: 28
A deep segmentation network for crack detection with progressive and hierarchical context fusion
Lei Yang, Hanyun Huang, Shuyi Kong, et al.
Journal of Building Engineering (2023) Vol. 75, pp. 106886-106886
Closed Access | Times Cited: 25
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: 23
The Crack Diffusion Model: An Innovative Diffusion-Based Method for Pavement Crack Detection
Haoyuan Zhang, Ning Chen, Mei Li, et al.
Remote Sensing (2024) Vol. 16, Iss. 6, pp. 986-986
Open Access | Times Cited: 10
CGV-Net: Tunnel Lining Crack Segmentation Method Based on Graph Convolution Guided Transformer
Kai Liu, Tao Ren, Zhangli Lan, et al.
Buildings (2025) Vol. 15, Iss. 2, pp. 197-197
Open Access | Times Cited: 1
A method for segmentation of tumors in breast ultrasound images using the variant enhanced deep learning
Ademola E. Ilesanmi, Utairat Chaumrattanakul, Stanislav S. Makhanov
Journal of Applied Biomedicine (2021) Vol. 41, Iss. 2, pp. 802-818
Closed Access | Times Cited: 53
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