基于机器视觉的筒子纱密度在线检测系统

doi:10.13475/j.fzxb.20190801106

摘要/Abstract

摘要：

为提高筒子纱密度检测自动化程度,解决传统筒子纱密度测量方法效率低、操作复杂等问题,设计了一种基于机器视觉的筒子纱密度在线检测系统。该系统由质量传感器、光电传感器、蓝色面光源、工业摄像机、传送装置和工控机组成。研究了筒子纱图像校正算法,根据透视投影理论建立了筒子纱校正模型,还原了筒子纱上下边界的直线特性,得到了理想的筒子纱侧面图像,用积分法得到筒子纱的精确体积。150个筒子纱密度检测结果表明:通过像素当量折算出筒子纱实际最大直径和体积参数,再结合高精度质量传感器的数据,最终可计算出筒子纱密度,基于机器视觉的筒子纱密度在线检测系统的检测精度和稳定性能可满足生产要求。

关键词: 筒子纱密度, 机器视觉, 在线检测, 筒子纱校正模型

Abstract:

In order to solve the problems of low efficiency and complex operation of the traditional measurement methods of package density, and improve the automation of the detection of package density, an on-line detection system for cheese package yarn density based on machine vision was designed. The system consists of a weight sensor, a photoelectric sensor, a blue light source, an industrial camera, a transmission device and an industrial computer. This research studied the image correction algorithm of the package yarn, established the package yarn correction model according to the perspective projection theory, restored the linear characteristics of the upper and lower boundaries of the package yarn, obtained the ideal side image of the package yarn, and achieved the accurate volume of the package yarn by the integration method. Through testing yarn density of 150 cheese packages, it is shown that the yarn density is able to be calculated by combining the weight data of high-precision weight sensor through converting the actual maximum diameter and volume parameters to pixel equivalent. The work illustrates that the detection system is able to detect the yarn package density online with acceptable detection accuracy and stability.

Key words: cheese yarn package density, machine vision, on-line detection, cheese yarn corrected model

中图分类号:

TS152.2

张建新, 李琦. 基于机器视觉的筒子纱密度在线检测系统[J]. 纺织学报, 2020, 41(06): 141-146.

ZHANG Jianxin, LI Qi. Online cheese package yarn density detection system based on machine vision[J]. Journal of Textile Research, 2020, 41(06): 141-146.

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序号	密度参考值/(g·cm^-3)	稳定性系数
1	0.465	0.012
2	0.470	0.014
3	0.467	0.021
4	0.488	0.006
5	0.452	0.003
6	0.500	0.022
7	0.498	0.039
8	0.458	0.003
9	0.477	0.031
10	0.455	0.030