Flow Measurements Derived from Camera Footage Using an Open-Source Ecosystem

Robert Meier,Franz Tscheikner-Gratl,Christos Makropoulos,David B Steffelbauer

doi:10.3390/w14030424

Robert Meier, Franz Tscheikner-Gratl + Show 2 more

Open Access

https://doi.org/10.3390/w14030424

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Abstract

Sensors used for wastewater flow measurements need to be robust and are, consequently, expensive pieces of hardware that must be maintained regularly to function correctly in the hazardous environment of sewers. Remote sensing can remedy these issues, as the lack of direct contact between sensor and sewage reduces the hardware demands and need for maintenance. This paper utilizes off-the-shelf cameras and machine learning algorithms to estimate the discharge in open sewer channels. We use convolutional neural networks to extract the water level and surface velocity from camera images directly, without the need for artificial markers in the sewage stream. Under optimal conditions, our method estimates the water level with an accuracy of ±2.48% and the surface velocity with an accuracy of ±2.08% in a laboratory setting—a performance comparable to other state-of-the-art solutions (e.g., in situ measurements).

Highlights

IntroductionCally expensive and require regular maintenance [1]
Traditional sewer flow sensors are typi‐cally expensive and require regular maintenance [1]
The water level predictions rely on single frames and result in 25 predictions per second, contrary to the surface velocity prediction which relies on a series of images, resulting in one prediction per second

Summary

Introduction

Cally expensive and require regular maintenance [1]. Regular maintenance is needed to keep the sensors operational or at least able to deliver meaning‐ ful data. Many countries require explosion proofing—e.g., ATEX [2]—of com‐ plex electronic equipment used in sewers, which further increases the costs. These aspects contribute to high initial investments and recurring maintenance expenses, respectively. Since the direct contact between sensor and sewage, or at least the possibility of it, is the primary source of these costs, the logical solution is to use remote sensing techniques, i.e., sensors which are physically separated from the sewage. Sensors based on ultrasound [3], camera images [4], or infrared [5] can be viable options

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