论文标题
具有神经形态动态视觉传感器序列的人工雨滴的直径和速度
Measuring diameters and velocities of artificial raindrops with a neuromorphic dynamic vision sensor disdrometer
论文作者
论文摘要
需要研究和校正天气雷达和微波连接的降雨估计值，需要测量降水量的大小和速度分布的水及其。现有的视频揭示仪测量滴剂尺寸分布，但低估了小雨滴，对于广泛的始终在物联网部署中是不切实际的。我们提出了一种使用神经形态事件摄像头测量液滴尺寸和速度的创新方法。这些动态视觉传感器异步输出的像素亮度稀疏流变化。液滴从焦点平面上掉落会产生由液滴运动产生的事件。液滴的大小和速度是从事件流推断的。使用改进的硬盘臂执行器可靠地产生人造雨滴，我们的实验显示了从0.3至2.5 mm的液滴尺寸，从1.3 m/s到8.0 m/s的液滴尺寸为7％（最大平均绝对百分比误差）。每个液滴只需要几百到数千个事件的处理，有可能使低功率始终在台词仪中消耗与降雨率成比例的功率。
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.
