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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:

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

Showing 1-25 of 40 citing articles:

An end-to-end computer vision system based on deep learning for pavement distress detection and quantification
Saúl Cano-Ortiz, L. Lloret Iglesias, P. Martínez Ruiz del Árbol, et al.
Construction and Building Materials (2024) Vol. 416, pp. 135036-135036
Open Access | Times Cited: 11
Automation in road distress detection, diagnosis and treatment
Xu Yang, Jianqi Zhang, Wenbo Liu, et al.
Journal of Road Engineering (2024) Vol. 4, Iss. 1, pp. 1-26
Open Access | Times Cited: 10
Stereoscopic monitoring of transportation infrastructure
Jianzhu Wang, Shuo Zhang, Hongyu Guo, et al.
Automation in Construction (2024) Vol. 164, pp. 105472-105472
Closed Access | Times Cited: 9
Real-time instance-level detection of asphalt pavement distress combining space-to-depth (SPD) YOLO and omni-scale network (OSNet)
Jiale Li, Chenglong Yuan, Xuefei Wang
Automation in Construction (2023) Vol. 155, pp. 105062-105062
Closed Access | Times Cited: 21
Development of a random forest based algorithm for road health monitoring
Revanth Dugalam, Guru Prakash
Expert Systems with Applications (2024) Vol. 251, pp. 123940-123940
Closed Access | Times Cited: 6
Computer vision in drone imagery for infrastructure management
Naveed Ejaz, Salimur Choudhury
Automation in Construction (2024) Vol. 163, pp. 105418-105418
Open Access | Times Cited: 5
A Method for Surveying Road Pavement Distress Based on Front-View Image Data Using a Lightweight Segmentation Approach
Yuanji Yang, Hui Wang, Junyang Kang, et al.
Journal of Computing in Civil Engineering (2024) Vol. 38, Iss. 5
Closed Access | Times Cited: 5
Multi-visual modality micro drone-based structural damage detection
Isaac Osei Agyemang, Liaoyuan Zeng, Jianwen Chen, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108460-108460
Closed Access | Times Cited: 4
Instrumentation and testing for road condition monitoring – A state-of-the-art review
Diego Maria Barbieri, Baowen Lou
NDT & E International (2024) Vol. 146, pp. 103161-103161
Open Access | Times Cited: 4
Detecção de defeitos utilizando YOLOv8 para determinação da condição de pavimentos asfálticos
Átila Marconcine de Souza, Carlos Eduardo de Oliveira, Pedro Henrique Bruder Decker, et al.
Revista ALCONPAT (2025) Vol. 15, Iss. 1, pp. 79-91
Open Access
Advancing smart transportation: A review of computer vision and photogrammetry in learning-based dimensional road pavement defect detection
Adamu Tafida, Wesam Salah Alaloul, Noor Amila Wan Abdullah Zawawi, et al.
Computer Science Review (2025) Vol. 56, pp. 100729-100729
Closed Access
A Generative Approach to Generalize Deep Learning Models for Pavement Distress Segmentation
Abhishek Kumar Prajapati, Ilgin Guler
Data Science for Transportation (2025) Vol. 7, Iss. 1
Closed Access
Uas-Based Detection and Quantification of Pavement Roughness and Vertical Deviations Due to Frost Heave
Farah Zaremotekhases, Adam G. Hunsaker, Eshan Dave, et al.
(2025)
Closed Access
Generative adversarial network for real-time identification and pixel-level annotation of highway pavement distresses
Mark Amo-Boateng, Yaw Adu‐Gyamfi
Automation in Construction (2025) Vol. 174, pp. 106122-106122
Closed Access
Smartphone applications for pavement condition monitoring: A review
Abdulnaser M. Al-Sabaeei, Mena I. Souliman, Ajayshankar Jagadeesh
Construction and Building Materials (2023) Vol. 410, pp. 134207-134207
Open Access | Times Cited: 10
Automated Pavement Condition Index Assessment with Deep Learning and Image Analysis: An End-to-End Approach
Eldor Ibragimov, Yongsoo Kim, Jeong Yong Lee, et al.
Sensors (2024) Vol. 24, Iss. 7, pp. 2333-2333
Open Access | Times Cited: 3
Automated anomaly detection of catenary split pins using unsupervised learning
Yunpeng Wu, Fanteng Meng, Yong Qin, et al.
Automation in Construction (2024) Vol. 165, pp. 105589-105589
Closed Access | Times Cited: 3
Smart Asphalt Mixtures: A Bibliometric Analysis of the Research Trends
Iran Rocha Segundo, Élida Margalho, Orlando Lima, et al.
Coatings (2023) Vol. 13, Iss. 8, pp. 1396-1396
Open Access | Times Cited: 8
Investigation of Typical Distresses of Flexible Pavements in Dhaka City and Possible Remedies
Ahmed Sajid Hasan, Kaniz Roksana, Sk. Faisal Kabir, et al.
International Journal of Pavement Research and Technology (2024)
Closed Access | Times Cited: 2
A 3D reconstruction-based method using unmanned aerial vehicles for the representation and analysis of road sections
Brahim Benmhahe, Jihane Alami Chentoufi, Mohamed Amine Basmassi
International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering (2024) Vol. 14, Iss. 2, pp. 1552-1552
Open Access | Times Cited: 2
YOLOX-RDD: A Method of Anchor-Free Road Damage Detection for Front-View Images
Jie Li, Zhong Qu, Shiyan Wang, et al.
IEEE Transactions on Intelligent Transportation Systems (2024) Vol. 25, Iss. 10, pp. 14725-14739
Closed Access | Times Cited: 2
Performance Evaluation of Convolutional Neural Network Models for Classification of Highway Hidden Distresses with GPR B-Scan Images
Guanglai Jin, Qilin Liu, Cai Wen-long, et al.
Applied Sciences (2024) Vol. 14, Iss. 10, pp. 4226-4226
Open Access | Times Cited: 2
Research on high-precision recognition model for multi-scene asphalt pavement distresses based on deep learning
Sheng Zhang, Zhenghao Bei, Tonghua Ling, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 2
An extensive bibliometric analysis of pavement deterioration detection using sensors and machine learning: Trends, innovations, and future directions
Mehmet Rizelioğlu
Alexandria Engineering Journal (2024) Vol. 112, pp. 349-366
Closed Access | Times Cited: 2
Semi-supervised crack detection using segment anything model and deep transfer learning
Jiale Li, Chenglong Yuan, Xuefei Wang, et al.
Automation in Construction (2024) Vol. 170, pp. 105899-105899
Closed Access | Times Cited: 2
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