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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:

Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement
Zhun Fan, Chong Li, Ying Chen, et al.
Coatings (2020) Vol. 10, Iss. 2, pp. 152-152
Open Access | Times Cited: 119

Showing 1-25 of 119 citing articles:

Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review
Mohsen Azimi, Armin Dadras Eslamlou, Gökhan Pekcan
Sensors (2020) Vol. 20, Iss. 10, pp. 2778-2778
Open Access | Times Cited: 465
A critical review and comparative study on image segmentation-based techniques for pavement crack detection
Narges Kheradmandi, Vida Mehranfar
Construction and Building Materials (2022) Vol. 321, pp. 126162-126162
Closed Access | Times Cited: 234
Performance Evaluation of Deep CNN-Based Crack Detection and Localization Techniques for Concrete Structures
Luqman Ali, Fady Alnajjar, Hamad Al Jassmi, et al.
Sensors (2021) Vol. 21, Iss. 5, pp. 1688-1688
Open Access | Times Cited: 212
DMA-Net: DeepLab With Multi-Scale Attention for Pavement Crack Segmentation
Xinzi Sun, Yuanchang Xie, Liming Jiang, et al.
IEEE Transactions on Intelligent Transportation Systems (2022) Vol. 23, Iss. 10, pp. 18392-18403
Closed Access | Times Cited: 139
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
Automatic concrete crack segmentation model based on transformer
Wenjun Wang, Chao Su
Automation in Construction (2022) Vol. 139, pp. 104275-104275
Closed Access | Times Cited: 108
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
Road damage detection using super-resolution and semi-supervised learning with generative adversarial network
Seungbo Shim, Jin Kim, Seong-Won Lee, et al.
Automation in Construction (2022) Vol. 135, pp. 104139-104139
Open Access | Times Cited: 73
A hybrid deep learning pavement crack semantic segmentation
Zaid Al‐Huda, Bo Peng, Riyadh Nazar Ali Algburi, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 122, pp. 106142-106142
Closed Access | Times Cited: 66
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
Pavement crack instance segmentation using YOLOv7-WMF with connected feature fusion
Guanting Ye, Sai Li, Manxu Zhou, et al.
Automation in Construction (2024) Vol. 160, pp. 105331-105331
Closed Access | Times Cited: 19
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: 18
Automatic Crack Detection on Road Pavements Using Encoder-Decoder Architecture
Zhun Fan, Chong Li, Ying Chen, et al.
Materials (2020) Vol. 13, Iss. 13, pp. 2960-2960
Open Access | Times Cited: 112
A Deeply Supervised Convolutional Neural Network for Pavement Crack Detection With Multiscale Feature Fusion
Zhong Qu, Chong Cao, Ling Liu, et al.
IEEE Transactions on Neural Networks and Learning Systems (2021) Vol. 33, Iss. 9, pp. 4890-4899
Closed Access | Times Cited: 95
Semi-supervised semantic segmentation network for surface crack detection
Wenjun Wang, Chao Su
Automation in Construction (2021) Vol. 128, pp. 103786-103786
Closed Access | Times Cited: 87
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
Pavement Crack Detection Method Based on Deep Learning Models
Guo X. Hu, Bao Long Hu, Zhong Yang, et al.
Wireless Communications and Mobile Computing (2021) Vol. 2021, Iss. 1
Open Access | Times Cited: 80
Optimized deep encoder-decoder methods for crack segmentation
Jacob König, Mark David Jenkins, Mike Mannion, et al.
Digital Signal Processing (2020) Vol. 108, pp. 102907-102907
Open Access | Times Cited: 73
Pixel-level pavement crack segmentation with encoder-decoder network
Youzhi Tang, Allen Zhang, Lei Luo, et al.
Measurement (2021) Vol. 184, pp. 109914-109914
Closed Access | Times Cited: 68
Automated defect inspection of concrete structures
Jun Kang Chow, Kuan-Fu Liu, Pin Siang Tan, et al.
Automation in Construction (2021) Vol. 132, pp. 103959-103959
Closed Access | Times Cited: 65
Sensor data-driven structural damage detection based on deep convolutional neural networks and continuous wavelet transform
Zuoyi Chen, Yanzhi Wang, Jun Wu, et al.
Applied Intelligence (2021) Vol. 51, Iss. 8, pp. 5598-5609
Closed Access | Times Cited: 64
Use of Parallel ResNet for High-Performance Pavement Crack Detection and Measurement
Zhun Fan, Huibiao Lin, Chong Li, et al.
Sustainability (2022) Vol. 14, Iss. 3, pp. 1825-1825
Open Access | Times Cited: 61
Semantic segmentation of cracks: Data challenges and architecture
Fabio Panella, Aldo Lipani, J. Boehm
Automation in Construction (2022) Vol. 135, pp. 104110-104110
Open Access | Times Cited: 58
Inspecting Buildings Using Drones and Computer Vision: A Machine Learning Approach to Detect Cracks and Damages
Hafiz Suliman Munawar, Fahim Ullah, Amirhossein Heravi, et al.
Drones (2021) Vol. 6, Iss. 1, pp. 5-5
Open Access | Times Cited: 57
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