Enhancing Water Level Identification with a Barcode-Patterned Panel and Machine Learning

  • Gabriel Montagni Domingues Filho Universidade de São Paulo
  • Caetano Mazzoni Ranieri Universidade de São Paulo
  • Douglas Queiroz Galucio Batista Universidade de São Paulo
  • Jó Ueyama Universidade de São Paulo


Floods cause significant material and human losses worldwide, leading to research in monitoring water levels in urban streams. Existing technologies, such as pressure and ultrasonic sensors, are accurate but costly to deploy. Although ground cameras offer a low-cost alternative, current approaches relying on weak visual markers are sensitive to environmental factors. We address this research gap by introducing a physical marker, called "barcode panel", which is a stainless steel plate with printed black stripes indicating water level. A deep learning algorithm was employed to accurately predict water levels based on this marker. We evaluated our approach using two datasets: one in a pool and another in an actual river. The model demonstrated precise water level predictions in the pool dataset and good results for the river dataset, despite training solely on the pool images. These promising results provide valuable insights for further studies and practical applications.

Palavras-chave: computer vision, deep learning, flood management, visual marker, water gauge


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MONTAGNI DOMINGUES FILHO, Gabriel; RANIERI, Caetano Mazzoni; BATISTA, Douglas Queiroz Galucio; UEYAMA, Jó. Enhancing Water Level Identification with a Barcode-Patterned Panel and Machine Learning. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 20. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 668-682. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2023.234311.