In recent years, traditional image processing techniques have seen the introduction of novel tools, able to face issues that are not always handy with classical vision algorithms. For example, classical image processing algorithms (measurement, detection of features, and many others) require a controlled environment, like illumination, target positioning, and vibration that can influence the scene for the correct operation. On the other hand, the machine learning approaches enabled image processing techniques also in non-controlled environments. One of these applications can be represented by developing a leak detector at the household level, based on processing pictures of the mechanical water meter dial. The proposed research investigates using a deep learning approach to detect the minimal movement of the water meter needles related to water leakage. In particular, a CNN was trained to correlate successive differences on the water meter dial images taken with an applied calibrated water flow. From this analysis, it is possible to detect the absence of periods with null consumption and thus detect small water losses.

A novel image processing technique based on deep learning for water consumption detection

Carratu' M.;Di Leo G.;Gallo V.;Liguori C.;Pietrosanto A.
2022

Abstract

In recent years, traditional image processing techniques have seen the introduction of novel tools, able to face issues that are not always handy with classical vision algorithms. For example, classical image processing algorithms (measurement, detection of features, and many others) require a controlled environment, like illumination, target positioning, and vibration that can influence the scene for the correct operation. On the other hand, the machine learning approaches enabled image processing techniques also in non-controlled environments. One of these applications can be represented by developing a leak detector at the household level, based on processing pictures of the mechanical water meter dial. The proposed research investigates using a deep learning approach to detect the minimal movement of the water meter needles related to water leakage. In particular, a CNN was trained to correlate successive differences on the water meter dial images taken with an applied calibrated water flow. From this analysis, it is possible to detect the absence of periods with null consumption and thus detect small water losses.
978-1-6654-8360-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4807746
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