Real-time dust monitoring for industrial site based on machine visio
-
摘要: 针对传统粉尘监测方法实时性差、覆盖面不全的问题，提出了2种基于机器视觉的工矿现场粉尘实时监测系统设计方案，即基于单目视觉的粉尘监测系统和基于双目视觉的粉尘监测系统。基于单目视觉的粉尘监测系统采用帧差法、腐蚀膨胀算法等实现对视场内粉尘目标的快速识别；基于双目视觉的粉尘监测系统在单目视觉粉尘监测系统的基础上，利用标定靶，通过空间三维重建实现粉尘定位。实验结果表明，基于单目视觉的粉尘监测系统可以捕捉粉尘团生成的过程，实时处理速率为4帧/s；而基于双目视觉的粉尘监测系统可以进一步测量粉尘团位置信息，定位误差在10%以内。Abstract: In view of problems of poor real-time performance and incomplete coverage of traditional dust monitoring methods, two kinds of design scheme of dust monitoring system based on machine vision were proposed, namely dust monitoring systems based on monocular vision and binocular vision. The dust monitoring system based on monocular vision uses frame difference method and corrosion expansion algorithm to realize rapid recognition of the dust target in the field of view. Based on monocular vision, the dust monitoring system based on binocular vision uses calibration target and three-dimensional space reconstruction to achieve dust positioning. The experimental results show that the dust monitoring system based on monocular vision can capture formation process of dust cluster, and the real-time processing rate is four frames per second; the dust monitoring system based on binocular vision can further measure the position information of dust clusters, and positioning error is less than 10%.
-
-
期刊类型引用(2)
1. 张红涛. 电牵引采煤机数据采集系统设计. 煤矿机械. 2025(04): 28-32 . 
百度学术
2. 张俊华，薛辉. 基于无线网络的心理教育资源传输和分布式方法. 自动化技术与应用. 2023(11): 114-117 . 百度学术
其他类型引用(0)
计量
- 文章访问数: 105
- HTML全文浏览量: 8
- PDF下载量: 27
- 被引次数: 2
下载:
