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Article: Detecting, extracting and classifying foreign objects in inter-basin channels to ensure water supply safety

TitleDetecting, extracting and classifying foreign objects in inter-basin channels to ensure water supply safety
Authors
KeywordsComputer vision
Foreign objects
Object detection
Unmanned aerial vehicle (UAV)
Water quality
Water supply safety
Issue Date2022
Citation
Journal of Hydroinformatics, 2022, v. 24, n. 1, p. 113-127 How to Cite?
AbstractForeign objects (e.g., livestock, rafting, and vehicles) intruded into inter-basin channels pose threats to water quality and water supply safety. Timely detection of the foreign objects and acquiring relevant information (e.g., quantities, geometry, and types) is a premise to enforce proactive measures to control potential loss. Large-scale water channels usually span a long distance and hence are difficult to be efficiently covered by manual inspection. Applying unmanned aerial vehicles for inspection can provide time-sensitive aerial images, from which intrusion incidents can be visually pinpointed. To automate the processing of such aerial images, this paper aims to propose a method based on computer vision to detect, extract, and classify foreign objects in water channels. The proposed approach includes four steps, i.e., aerial image preprocessing, abnormal region detection, instance extraction, and foreign object classification. Experiments demonstrate the efficacy of the approach, which can recognize three typical foreign objects (i.e., livestock, rafting, and vehicle) with a robust performance. The proposed approach can raise early awareness of intrusion incidents in water channels for water quality assurance.
Persistent Identifierhttp://hdl.handle.net/10722/324210
ISSN
2023 Impact Factor: 2.2
2023 SCImago Journal Rankings: 0.573
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChen, Junjie-
dc.contributor.authorLiu, Donghai-
dc.date.accessioned2023-01-13T03:02:14Z-
dc.date.available2023-01-13T03:02:14Z-
dc.date.issued2022-
dc.identifier.citationJournal of Hydroinformatics, 2022, v. 24, n. 1, p. 113-127-
dc.identifier.issn1464-7141-
dc.identifier.urihttp://hdl.handle.net/10722/324210-
dc.description.abstractForeign objects (e.g., livestock, rafting, and vehicles) intruded into inter-basin channels pose threats to water quality and water supply safety. Timely detection of the foreign objects and acquiring relevant information (e.g., quantities, geometry, and types) is a premise to enforce proactive measures to control potential loss. Large-scale water channels usually span a long distance and hence are difficult to be efficiently covered by manual inspection. Applying unmanned aerial vehicles for inspection can provide time-sensitive aerial images, from which intrusion incidents can be visually pinpointed. To automate the processing of such aerial images, this paper aims to propose a method based on computer vision to detect, extract, and classify foreign objects in water channels. The proposed approach includes four steps, i.e., aerial image preprocessing, abnormal region detection, instance extraction, and foreign object classification. Experiments demonstrate the efficacy of the approach, which can recognize three typical foreign objects (i.e., livestock, rafting, and vehicle) with a robust performance. The proposed approach can raise early awareness of intrusion incidents in water channels for water quality assurance.-
dc.languageeng-
dc.relation.ispartofJournal of Hydroinformatics-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectComputer vision-
dc.subjectForeign objects-
dc.subjectObject detection-
dc.subjectUnmanned aerial vehicle (UAV)-
dc.subjectWater quality-
dc.subjectWater supply safety-
dc.titleDetecting, extracting and classifying foreign objects in inter-basin channels to ensure water supply safety-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.2166/HYDRO.2021.118-
dc.identifier.scopuseid_2-s2.0-85124845666-
dc.identifier.volume24-
dc.identifier.issue1-
dc.identifier.spage113-
dc.identifier.epage127-
dc.identifier.eissn1465-1734-
dc.identifier.isiWOS:000728891000001-

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