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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:

Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features
Song Wei-dong, Guohui Jia, Hong Zhu, et al.
Journal of Advanced Transportation (2020) Vol. 2020, pp. 1-11
Open Access | Times Cited: 110

Showing 1-25 of 110 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
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
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
Pixel-level road crack detection in UAV remote sensing images based on ARD-Unet
Yuxi Gao, Hongbin Cao, Weiwei Cai, et al.
Measurement (2023) Vol. 219, pp. 113252-113252
Closed Access | Times Cited: 40
Survey on performance of deep learning models for detecting road damages using multiple dashcam image resources
Minh-Tu Cao, Quoc‐Viet Tran, Ngoc‐Mai Nguyen, et al.
Advanced Engineering Informatics (2020) Vol. 46, pp. 101182-101182
Closed Access | Times Cited: 105
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
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
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
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
Automated Asphalt Highway Pavement Crack Detection Based on Deformable Single Shot Multi-Box Detector Under a Complex Environment
Kun Yan, Zhihua Zhang
IEEE Access (2021) Vol. 9, pp. 150925-150938
Open Access | Times Cited: 55
Automatic pavement damage predictions using various machine learning algorithms: Evaluation and comparison
Ritha Nyirandayisabye, Huixia Li, Qiming Dong, et al.
Results in Engineering (2022) Vol. 16, pp. 100657-100657
Open Access | Times Cited: 42
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
Encoder–decoder with pyramid region attention for pixel‐level pavement crack recognition
Hui Yao, Yanhao Liu, Haotian Lv, et al.
Computer-Aided Civil and Infrastructure Engineering (2023) Vol. 39, Iss. 10, pp. 1490-1506
Open Access | Times Cited: 23
Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open- Access Papers
Nils Hütten, Miguel Alves Gomes, Florian Hölken, et al.
Applied System Innovation (2024) Vol. 7, Iss. 1, pp. 11-11
Open Access | Times Cited: 13
Crack damage prediction of asphalt pavement based on tire noise: A comparison of machine learning algorithms
Huixia Li, Ritha Nyirandayisabye, Qiming Dong, et al.
Construction and Building Materials (2024) Vol. 414, pp. 134867-134867
Closed Access | Times Cited: 12
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
Multi-scale context feature and cross-attention network-enabled system and software-based for pavement crack detection
Xin Wen, Shuo Li, Hao Yu, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 127, pp. 107328-107328
Closed Access | Times Cited: 19
Advanced industrial informatics towards smart, safe and sustainable roads: A state of the art
Hui Yao, Zijin Xu, Yue Hou, et al.
Journal of Traffic and Transportation Engineering (English Edition) (2023) Vol. 10, Iss. 2, pp. 143-158
Open Access | Times Cited: 18
Machine Learning for Prediction of the International Roughness Index on Flexible Pavements: A Review, Challenges, and Future Directions
Tiago Tamagusko, Adelino Ferreira
Infrastructures (2023) Vol. 8, Iss. 12, pp. 170-170
Open Access | Times Cited: 16
Hybrid Pixel‐Level Crack Segmentation for Ballastless Track Slab Using Digital Twin Model and Weakly Supervised Style Transfer
Wenbo Hu, Weidong Wang, Xianhua Liu, et al.
Structural Control and Health Monitoring (2024) Vol. 2024, Iss. 1
Open Access | Times Cited: 5
Development of an Algorithm for Analysing the Condition of the Road Surface Using Artificial Intelligence
Artеm Rada, Nikolaj Kon’kov
World of Transport and Transportation (2025) Vol. 22, Iss. 2, pp. 40-46
Closed Access
Dual Model for International Roughness Index Classification and Prediction
Noelia Molinero-Pérez, Laura Montalbán-Domingo, Amalia Sanz-Benlloch, et al.
Infrastructures (2025) Vol. 10, Iss. 1, pp. 23-23
Open Access
Classification of Pavement Distress Images Using Fusion Convolutional Neural Network of Dual Branch
Ziyi Zhang, Chihang Zhao, Yongjun Shao, et al.
SAE technical papers on CD-ROM/SAE technical paper series (2025) Vol. 1
Closed Access
Structural Crack Detection from Benchmark Data Sets Using Pruned Fully Convolutional Networks
Xiao‐Wei Ye, Tao Jin, Z. X. Li, et al.
Journal of Structural Engineering (2021) Vol. 147, Iss. 11
Closed Access | Times Cited: 38
Computer Vision-Based Bridge Damage Detection Using Deep Convolutional Networks with Expectation Maximum Attention Module
Wenting Qiao, Biao Ma, Qiangwei Liu, et al.
Sensors (2021) Vol. 21, Iss. 3, pp. 824-824
Open Access | Times Cited: 37
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