一种模拟飞虫视觉运动感知的U-LSPIV测量系统
A U-LSPIV Measurement System Simulating Visual Motion Perception of Flying Insects

DOI: 10.12677/CSA.2021.117193, PP. 1888-1895

Keywords: 河面流速场测量，无人机，大尺度粒子图像测速，飞虫复眼
Measurement of River Surface Velocity Field, Unmanned Aerial Vehicle, Large-Scale Particle Image Velocimetry, Flying Insect’s Compound Eye

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Abstract:

通过光学成像观测水流示踪(物)目标，进而测量河流表面二维流速场技术，即LSPIV (large-scale particle image velocimetry)技术，它与常规的转子流速仪、声学、雷达和激光多普勒测速等技术和方法相比，具有诸多优点。然而，目前大多数的LSPIV系统是基于地面固定摄像头测量的，即使是采用基于无人机(unmanned aerial vehicle)的测量，在万平方米以上的超大尺度水面流场成像测量应用中，因受到施测作业区域、天气及光照、水流条件、河–气界面(大气–水体交界面)成像环境下杂乱光线混叠扰动等因素的影响，测量的精度及其稳定性严重受限。为此，本文提出一种模拟飞行昆虫–蜻蜓复眼视觉运动感知的U-LSPIV测量系统设计模式，采用DJI大疆如风系列WIND 8无人机搭载测量仪器，基于光学成像观测水面，通过模拟飞行昆虫–蜻蜓复眼从水面杂乱光线混叠扰动的背景中准确辨识感兴趣目标，所具有的高适应性和高可靠性的自然特性，以及借鉴轻量和低功率的视叶(lightweight and low-powered optic lobe)神经计算范式，以提升水面全场流速场及断面流量的测量精度和稳定性。本文提出的新颖设计模式为适应于野外环境超大尺度的LSPIV测量应用提供了有效的解决方案。
Large-scale particle image velocimetry (LSPIV) is a two-dimensional velocity field technology to measure the river surface by optical imaging observation of flow tracer (object) target, which has many advantages compared with the conventional rotor velocimeter, acoustics, radar and laser Doppler velocimetry. However, at present, most of the LSPIV system is based on the ground fixed camera measurement, even if the measurement is based on unmanned aerial vehicle. In the application of ultra-large scale surface flow field imaging measurement over ten thousand square meters, the accuracy and stability of the measurement are severely limited due to the influence of the operating area, weather and illumination, water flow conditions, and the disturbance of chaotic light aliasing in the imaging environment of the river-air interface (atmosphere-water interface). Therefore, this paper proposes a design pattern of U-LSPIV measurement system for simulating complex eye visual motion perception of flying insect-dragonfly, by using the DJI Enterprise WIND series WIND 8 UAVs carrying measuring instrument. Based on optical imaging observation of the water surface, by simulating compound eye of flying insect-dragonfly in the background of the disturbance of chaotic light aliasing to identify accurately the target of interest, the natural characteristics of high adaptability and high reliability, and the lightweight and low-power optic lobe neural compu-ting paradigm are used to improve the accuracy and stability of the measurement of the full-field velocity field and sectional flow in the water surface. The novel design pattern proposed in this paper provides an effective solution for the application of LSPIV measurement in the field environment at ultra-large scale.

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