Measuring diameters and velocities of artificial raindrops with a neuromorphic dynamic vision sensor disdrometer

TL;DR

This paper introduces a novel neuromorphic vision sensor-based disdrometer that accurately measures raindrop sizes and velocities with low power consumption, suitable for continuous IoT deployment.

Contribution

It presents an innovative method using a neuromorphic event camera to measure droplet size and velocity, achieving high accuracy and low power usage.

Findings

7% maximum mean absolute percentage error for droplet sizes and speeds

Low power consumption suitable for always-on IoT deployment

Effective measurement of small raindrops from 0.3 to 2.5 mm

Abstract

Hydrometers that can measure size and velocity distributions of precipitation are needed for research and corrections of rainfall estimates from weather radars and microwave links. Existing video disdrometers measure drop size distributions, but underestimate small raindrops and are impractical for widespread always-on IoT deployment. We propose an innovative method of measuring droplet size and velocity using a neuromorphic event camera. These dynamic vision sensors asynchronously output a sparse stream of pixel brightness changes. Droplets falling through the plane of focus create events generated by the motion of the droplet. Droplet size and speed are inferred from the stream of events. Using an improved hard disk arm actuator to reliably generate artificial raindrops, our experiments show small errors of 7% (maximum mean absolute percentage error) for droplet sizes from 0.3 to 2.5 mm and speeds from 1.3 m/s to 8.0 m/s. Each droplet requires the processing of only a few hundred to thousands of events, potentially enabling low-power always-on disdrometers that consume power proportional to the rainfall rate.