Deep Learning and object detection for water level measurement using patterned visual markers

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorDomingues Filho, G. M.-
Autor(es): dc.creatorRanieri, C. M.-
Autor(es): dc.creatorMatos, S. N.-
Autor(es): dc.creatorMeneguette, R. I.-
Autor(es): dc.creatorUeyama, J.-
Data de aceite: dc.date.accessioned2025-08-21T20:42:53Z-
Data de disponibilização: dc.date.available2025-08-21T20:42:53Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-10-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/TLA.2024.10738344-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/306661-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/306661-
Descrição: dc.descriptionFlooding is one of the most impactful natural disasters, causing significant losses and prompting extensive research into monitoring water levels in urban streams. Current technologies rely on pressure and ultrasonic sensors, which, while accurate, can be susceptible to damage from floods and are often costly. As an alternative, ground camera approaches offer a low-cost solution; however, most of these methods use raw images from the water stream and are sensitive to environmental factors. We address this gap with a dataset comprising a visual marker with black bars indicating the water level, which we refer to as barcode panel. We employed various deep learning algorithms to predict the water level and compared their performance. The proposed approach was evaluated using classic classification and error metrics. The models demonstrated accuracy in detecting the water level. These promising results provide important insights for practical applications and future studies.-
Descrição: dc.descriptionUniv Sao Paulo, Sao Carlos, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, Rio Claro, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, Rio Claro, Brazil-
Formato: dc.format892-898-
Idioma: dc.languageen-
Publicador: dc.publisherIeee-inst Electrical Electronics Engineers Inc-
Relação: dc.relationIeee Latin America Transactions-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectCameras-
Palavras-chave: dc.subjectFloods-
Palavras-chave: dc.subjectBars-
Palavras-chave: dc.subjectDetectors-
Palavras-chave: dc.subjectDeep learning-
Palavras-chave: dc.subjectVisualization-
Palavras-chave: dc.subjectWater resources-
Palavras-chave: dc.subjectAccuracy-
Palavras-chave: dc.subjectMeteorology-
Palavras-chave: dc.subjectClassification algorithms-
Palavras-chave: dc.subjectdeep learning-
Palavras-chave: dc.subjectcomputer vision-
Palavras-chave: dc.subjectflood management-
Palavras-chave: dc.subjectvisual marker-
Título: dc.titleDeep Learning and object detection for water level measurement using patterned visual markers-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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