Estimating water turbidity from a smartphone camera
File(s)
Accepted version
Author(s)
Maria Lozano Wilches, Lina
Jantarakasem, Chotiwat
Sioné, Laure
Templeton, Michael
Mikolajczyk, Krystian
Type
Conference Paper
Abstract
Water quality monitoring is indispensable for safeguarding human health. One aspect of water quality is turbidity, the measurement of which typically involves on-site water sampling and laboratory analysis, which may be both costly and labour-intensive in the context of developing countries. Alternative portable devices have been developed but they are often inconvenient and require technical expertise. In recent years, smartphone-based solutions have been developed with the aim of bringing turbidimeters to the wider population. However, they rely on additional equipment to create enclosed environments for the sample and the camera to remove ambient light. Therefore, turbidimeters in general require either technical expertise or additional equipment, which has limited their usage, especially in developing countries, where they are most needed.
In this paper we introduce a new benchmark with a new task for computer vision that aims at estimating a blur of a pattern observed through a liquid. We propose and
evaluate an approach for measuring water turbidity from a picture taken by a smartphone camera without any additional equipment. We design a simple protocol for taking a picture of a water sample that allows to estimate its turbidity, collect a dataset and design a benchmark for measuring the performance of computer vision methods in this task. Our model is able to accurately determine turbidity in the range of 0 - 40 NTU.
In this paper we introduce a new benchmark with a new task for computer vision that aims at estimating a blur of a pattern observed through a liquid. We propose and
evaluate an approach for measuring water turbidity from a picture taken by a smartphone camera without any additional equipment. We design a simple protocol for taking a picture of a water sample that allows to estimate its turbidity, collect a dataset and design a benchmark for measuring the performance of computer vision methods in this task. Our model is able to accurately determine turbidity in the range of 0 - 40 NTU.
Date Issued
2022-11-21
Date Acceptance
2022-10-13
Citation
Proceedings of the British Machine Vision Conference, 2022
Journal / Book Title
Proceedings of the British Machine Vision Conference
Copyright Statement
© 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.
Source
33rd British Machine Vision conference 2022
Publication Status
Published
Start Date
2022-11-21
Finish Date
2022-11-24