Academic Journal
Ensuring reliable water level measurement for flooding: A redundancy-based approach with pressure transducer and computer vision
| العنوان: | Ensuring reliable water level measurement for flooding: A redundancy-based approach with pressure transducer and computer vision |
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| المؤلفون: | Matos, Saulo Neves, Rocha, Arthur Lima Marques, Domingues Filho, Gabriel Montagni, Ranieri, Caetano Mazzoni, Garcia, Rodrigo Dutra, Faria, Ana Clara de Oliveira, Medina, Maria Mercedes Gamboa, Ueyama, Jó |
| المساهمون: | Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado de São Paulo |
| المصدر: | Transactions of the Institute of Measurement and Control ; ISSN 0142-3312 1477-0369 |
| بيانات النشر: | SAGE Publications |
| سنة النشر: | 2024 |
| الوصف: | Fluid level measurement is essential in many fields, including industrial and civil sectors, especially for urban flood detection, where there is a high risk of mortality and economic losses. However, although contact-based methods that employ pressure transducers can achieve a high degree of precision, they are susceptible to damage from direct contact with the fluid. This study adopts a redundancy-based approach that combines pressure transducer measurements with computer vision to provide enhanced reliability and reduce the risk of sensor failures. Our approach entails training a deep-learning model that uses pressure sensor data to mitigate this potential risk of damage and avoid the need for manually annotating sets of images. The results show that the pressure transducer has high accuracy, with a mean absolute error (MAE) of 1.21 cm, and that the computer vision model which is trained on pressure sensor data, achieves a comparable MAE of 6.67 cm. This approach also makes the system more robust and includes a dependable backup measurement method in case the primary sensor fails. Furthermore, the model trained on the sensor data led to results that were very similar to those trained directly on ground-truth data. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| DOI: | 10.1177/01423312241285952 |
| الاتاحة: | https://doi.org/10.1177/01423312241285952 https://journals.sagepub.com/doi/pdf/10.1177/01423312241285952 https://journals.sagepub.com/doi/full-xml/10.1177/01423312241285952 |
| Rights: | https://journals.sagepub.com/page/policies/text-and-data-mining-license |
| رقم الانضمام: | edsbas.61226733 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1177/01423312241285952 |
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