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基于施莱伦机器视觉系统的风速估计灵感来自温室顶风口

Huang Li¹, Angui Li¹, Linhua Zhang²

¹School of Building Equipment Science and Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.

Sensors (Basel, Switzerland)

|August 12, 2023

概括

这项研究提出了一种低成本的方法,使用施莱伦成像和加快强特征 (SURF) 匹配来估计温室风速. 该技术准确地跟踪空气流量,即使密度有差异,为农业通风监测提供了一个有前途的工具.

科学领域:

流体动力学流体动力学
光学测量技术的使用.
农业工程农业工程

背景情况:

温室通风对于作物生产和工人安全至关重要.
精确的风速估计对于优化通风系统至关重要.
现有的方法可能是昂贵的或不适合不同的密度条件.

研究的目的:

引入一种新的,低成本的方法来估计温室中的风速.
为了利用施莱伦技术结合加快强特征 (SURF) 匹配用于流体运动学分析.
评估该方法对具有显著温度和密度差异的空气流的有效性.

主要方法:

使用schlieren技术可视化空气流.
应用加快强大特征 (SURF) 匹配来跟踪相邻视频中的像素位移.
整合图像形态分析与SURF进行速度估计.
根据理论值和风速传感器数据验证结果.

主要成果:

该方法成功地估计了流和扰乱流体图像的风速.
实现了高精度,大约有500个有效的特征匹配点组,在理论无维速度的0.1差距内.
与实际风速变化有很好的相关性,估计误差一般在10%以内.

结论:

开发的方法为温室环境中风速估计提供了可行的,低成本的解决方案.
该技术能够处理不同密度的空气流,与温室通风口相关.
建议进行进一步的研究,以完善统计方法和实验设备,以便更广泛地应用.

关键词:

在 SURF 功能匹配中匹配.流体成像运动学流体成像运动学温室的温室效应是什么温室效应是什么斯克莱伦系统的系统.单通风通风系统的通风方式

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