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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:

Comparison of classic object-detection techniques for automated sewer defect detection
Qianqian Zhou, Zuxiang Situ, Shuai Teng, et al.
Journal of Hydroinformatics (2022) Vol. 24, Iss. 2, pp. 406-419
Open Access | Times Cited: 13

Showing 13 citing articles:

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: 16
PipeTransUNet: CNN and Transformer fusion network for semantic segmentation and severity quantification of multiple sewer pipe defects
Mingze Li, Mingchao Li, Qiubing Ren, et al.
Applied Soft Computing (2024) Vol. 159, pp. 111673-111673
Closed Access | Times Cited: 7
Automatic Detection Method of Sewer Pipe Defects Using Deep Learning Techniques
Jiawei Zhang, Xiang Liu, Xing Zhang, et al.
Applied Sciences (2023) Vol. 13, Iss. 7, pp. 4589-4589
Open Access | Times Cited: 14
Comparative Effectiveness of Data Augmentation Using Traditional Approaches versus StyleGANs in Automated Sewer Defect Detection
Qianqian Zhou, Zuxiang Situ, Shuai Teng, et al.
Journal of Water Resources Planning and Management (2023) Vol. 149, Iss. 9
Closed Access | Times Cited: 11
Multisensor data fusion approach for sediment assessment of sewers in operation
Chen Li, Ke Chen, Hanlin Li, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 132, pp. 107965-107965
Closed Access | Times Cited: 4
A Composite Transformer-Based Multi-Stage Defect Detection Architecture for Sewer Pipes
Zifeng Yu, Xianfeng Li, Lianpeng Sun, et al.
Computers, materials & continua/Computers, materials & continua (Print) (2024) Vol. 78, Iss. 1, pp. 435-451
Open Access | Times Cited: 4
Automated classification and localization of sewer pipe defects in small-sample CCTV imagery: an enhanced transformer-based framework
Qiubing Ren, Mingchao Li, Mingze Li, et al.
Journal of Civil Structural Health Monitoring (2025)
Closed Access
P-DETR: A Transformer-Based Algorithm for Pipeline Structure Detection
Ibrahim Akinjobi Aromoye, Lo Hai Hiung, Patrick Sébastian
Results in Engineering (2025), pp. 104652-104652
Open Access
Infrastructure automated defect detection with machine learning: a systematic review
Saeed Talebi, Song Wu, Arijit Sen, et al.
International Journal of Construction Management (2025), pp. 1-12
Open Access
Deep Learning for Automated Encrustation Detection in Sewer Inspection.
Wasiu Yusuf, Hafiz Alaka, Mubashir Ahmad, et al.
Intelligent Systems with Applications (2024), pp. 200433-200433
Open Access | Times Cited: 2
Visual blockage assessment at culverts using synthetic images to mitigate blockage-originated floods
Umair Iqbal, Johan Barthélemy, Pascal Perez
Journal of Hydroinformatics (2023) Vol. 25, Iss. 4, pp. 1531-1545
Open Access | Times Cited: 2
A Lightweight Method for Detecting Sewer Defects Based on Improved YOLOv5
Xing Zhang, Jiawei Zhang, Lei Tian, et al.
Applied Sciences (2023) Vol. 13, Iss. 15, pp. 8986-8986
Open Access | Times Cited: 2
A transformer cascaded model for defect detection of sewer pipes based on confusion matrix
Zifeng Yu, Xianfeng Li, Lianpeng Sun
Measurement Science and Technology (2024) Vol. 35, Iss. 11, pp. 115410-115410
Closed Access
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