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基于近场计算机视觉的喷射过程中射流轨迹实时监测。

Real-Time Monitoring of Jet Trajectory during Jetting Based on Near-Field Computer Vision.

机构信息

School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China.

Xu-gong Construction Machinery Group (XCMG) Research Institute, Xuzhou 221116, China.

出版信息

Sensors (Basel). 2019 Feb 8;19(3):690. doi: 10.3390/s19030690.

DOI:10.3390/s19030690
PMID:30744012
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6387458/
Abstract

A novel method of near-field computer vision (NFCV) was developed to monitor the jet trajectory during the jetting process, which was used to precisely predict the falling point position of the jet trajectory. By means of a high-resolution webcam, the NFCV sensor device collected near-field images of the jet trajectory. Preprocessing of collected images was carried out, which included squint image correction, noise elimination, and jet trajectory extraction. The features of the jet trajectory in the processed image were extracted, including: start-point slope (SPS), end-point slope (EPS), and overall trajectory slope (OTS) based on the proposed mean position method. A multiple regression jet trajectory range prediction model was established based on these trajectory characteristics and the reliability of the model was verified. The results show that the accuracy of the prediction model is not less than 94% and the processing time is less than 0.88, which satisfy the requirements of real-time online jet trajectory monitoring.

摘要

一种新颖的近场计算机视觉（NFCV）方法被开发出来，用于监测喷射过程中的射流轨迹，以便精确预测射流轨迹的降落点位置。NFCV 传感器装置通过高分辨率网络摄像头采集射流轨迹的近场图像。对采集到的图像进行预处理，包括斜视图像校正、噪声消除和射流轨迹提取。从处理后的图像中提取射流轨迹的特征，包括：基于提出的平均位置方法的起点斜率（SPS）、终点斜率（EPS）和整体轨迹斜率（OTS）。基于这些轨迹特征和模型的可靠性，建立了一个多元回归射流轨迹范围预测模型。结果表明，预测模型的精度不低于 94%，处理时间小于 0.88，满足实时在线射流轨迹监测的要求。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2906/6387458/c34b28575ade/sensors-19-00690-g011.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2906/6387458/77c9573f93b6/sensors-19-00690-g005.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2906/6387458/8d8f89a49a0b/sensors-19-00690-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2906/6387458/b5ba2174b768/sensors-19-00690-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2906/6387458/4023d77520ae/sensors-19-00690-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2906/6387458/1ac23d73cebd/sensors-19-00690-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2906/6387458/c34b28575ade/sensors-19-00690-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2906/6387458/38c4b9936036/sensors-19-00690-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2906/6387458/8e27bb714bd5/sensors-19-00690-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2906/6387458/91ffb86cdbb1/sensors-19-00690-g003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2906/6387458/77c9573f93b6/sensors-19-00690-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2906/6387458/094e0712faec/sensors-19-00690-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2906/6387458/8d8f89a49a0b/sensors-19-00690-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2906/6387458/b5ba2174b768/sensors-19-00690-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2906/6387458/4023d77520ae/sensors-19-00690-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2906/6387458/1ac23d73cebd/sensors-19-00690-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2906/6387458/c34b28575ade/sensors-19-00690-g011.jpg

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