AI-enabled Underground Water Pipe non -destructive Inspection-Reference-Cited by-同舟云学术

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AI-enabled Underground Water Pipe non -destructive Inspection

Published:2023-07-13 Issue:6 Volume:83 Page:18309-18332
ISSN:1573-7721
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Angelis Georgios-Fotios,Chorozoglou Dimitrios,Papadopoulos Stavros,Drosou Anastasios,Giakoumis Dimitrios,Tzovaras Dimitrios

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-023-15797-w.pdf

Reference84 articles.

1. Ahmed M, Hashmi KA, Pagani A, Liwicki M, Stricker D, Afzal MZ (2021) Survey and performance analysis of deep learning based object detection in challenging environments. Sensors 21(15):5116

2. Ruchti GF (2017) Water Pipeline Condition Assessment, 1st edn. Am Soc Civ Eng (ASCE), Virginia, United States

3. Alshamy HM, Sadah JWA, Saeed TR, Mohammed SA, Hatem GM, Gatan AH (2021) Evaluation of gpr detection for buried objects material with different depths and scanning angles. In IOP Conference Series: Mater Sci Eng, vol 1090, p 012042, IOP Publishing

4. Ayala-Cabrera D, Campbell E, Carreño-Alvarado E, Izquierdo J, Pérez-García R (2014) Water leakage evolution based on gpr interpretations. Procedia Engineering 89:304–310

5. Pham MT, Lefèvre S (2018) Buried object detection from b-scan ground penetrating radar data using faster-rcnn. In: IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, pp 6804–6807, IEEE

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Three-Dimensional Reconstruction of Water Leaks in Water Distribution Networks from Ground-Penetrating Radar Images by Exploring New Influencing Factors with Multi-Agent and Intelligent Data Analysis;The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024);2024-09-10

2. Early Drought Detection in Maize Using UAV Images and YOLOv8+;Drones;2024-04-24

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