纺织学报 ›› 2021, Vol. 42 ›› Issue (03): 115-121.doi: 10.13475/j.fzxb.20200502207

• 染整与化学品 • 上一篇    下一篇

基于机器视觉的单组分染液浓度快速检测方法

田宇航, 王绍宗(), 张文昌, 张倩   

  1. 机械科学研究总院 先进成形技术与装备国家重点实验室, 北京 100083
  • 收稿日期:2020-05-11 修回日期:2020-11-30 出版日期:2021-03-15 发布日期:2021-03-17
  • 通讯作者: 王绍宗
  • 作者简介:田宇航(1995—),女,硕士生。主要研究方向为先进制造技术与装备。
  • 基金资助:
    山东省重大科技创新项目(2019TSLH0204)

Rapid detection method of single-component dye liquor concentration based on machine vision

TIAN Yuhang, WANG Shaozong(), ZHANG Wenchang, ZHANG Qian   

  1. State Key Laboratory of Advanced Forming Technology and Equipment, China Academy of Machinery Science and Technology, Beijing 100083, China
  • Received:2020-05-11 Revised:2020-11-30 Online:2021-03-15 Published:2021-03-17
  • Contact: WANG Shaozong

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摘要：

针对印染行业的单组分染液浓度的检测需求,提出一种基于机器视觉的对单组分染液浓度进行快速检测的方法。该方法采用颜色亮度可调的背光光源和彩色工业相机搭建机器视觉检测平台,将质量浓度范围在0~0.4 g/L的单组分染液样品通过玻璃皿置于背光光源上进行图像采集和处理,获取染液颜色特征值(R、G、B值),拟合不同光源条件下的R、G、B值随染液浓度变化的曲线,以朗伯比尔定律为依据建立R、G、B值与染液浓度的关系模型,实现染液浓度的预测。结果表明,对于DRA-3R染料溶液,光源为蓝光(亮度等级为50)条件下建立的最小值模型预测精度最高,平均相对误差绝对值为3.35%,并且该方法检测速度快、成本低,为染液浓度在线检测的工业化应用提供了一定的研究基础。

关键词: 机器视觉, 彩色背光光源, 染液浓度检测, 染液, 颜色特征值

Abstract:

A method based on machine vision was proposed to detect the concentration of dye liquor with a single-component for the printing and dyeing industry.With this method, a machine vision detection platform was built with a color adjustable backlight source and an industrial color camera. The single-component dye liquor sample with a concentration range of 0-0.4 g/L was placed on a backlight light source through a glass dish for image acquisition and processing, in order to obtain the color characteristic values of the dye liquor, and to get the relationship between the RGB value and concentration of dye liquor under different light source conditions.Based on Lambert-Beer law, the relational model between the RGB value and dye liquor concentration was built to predict the concentration of dye in the dye liquor.It is verified by experimental fact that for the DRA-3R dye liquor, the minimum value model established under the condition of blue light intensity 50 has the highest prediction accuracy. The absolute value of the average relative error of the results is 3.35%. Experiments show that the method offers fast detection speed and low cost, which provides a certain research base for industrial application of on-line detection of dye liquor concentration.

Key words: machine vision, color backlight source, detection of dye liquor concentration, dye liquor, RGB value

中图分类号: 

  • TS190.4

图1

基于机器视觉的染液浓度检测装置图"

图2

染液浓度检测装置样机"

表1

标准样品R、G、B值"

染料型号 染液质量浓度/(g·L-1) R值 G值 B值
DRA-3B 0.4 205.77 27.6 8.27
DRA-3R 0.4 230.97 118.75 30.13
DRA-3G 0.2 4.26 22.86 49.43

图3

采用绿光光源和红光光源时R、G、B值与对应染液质量浓度拟合曲线"

图4

采用不同亮度等级的蓝光时R、G、B值与对应染液质量浓度拟合曲线"

表2

R、G、B值与染液质量浓度的回归方程及决定系数"

蓝光亮度等级 因变量 回归方程 决定系数r2
50 R y=78.551 03×10-4.038 84x 0.997 30
G y=410.039 33×10-3.122 55x 0.989 66
B y=2 121.081 35×10-4.575 25x 0.976 26
20 R y=27.567 31×10-4.204 28x 0.996 97
G y=163.335 7×10-3.374 1x 0.998 79
B y=690.187 ×10-4.410 42x 0.992 08
15 R y=20.579 4×10-4.569 75x 0.997 63
G y=122.772 88×10-3.624 28x 0.998 91
B y=444.944 75×10-4.304 12x 0.996 11
10 R y=13.768 9×10-4.314 87x 0.994 49
G y=84.228 05×10-3.363 2x 0.996 65
B y=13.768 9×10-4.043 69x 0.993 99

表3

线性回归方程及决定系数"

蓝光亮
度等级		 因变量 回归方程 决定系数r2
50 lg(R0/R) y=4.27157x-0.02536 0.991 2
20 lg(G0/G) y=3.55831x-0.0204 0.997 5
15 lg(G0/G) y=3.40354x+0.02737 0.993 3
10 lg(G0/G) y=4.52617x-0.22988 0.996 6

表4

线性模型验证结果"

蓝光亮
度等级		 染液质量浓度/(g·L-1) 相对
误差/%		 平均相对误差
绝对值/%
实际值 预测值
50 0.05 0.050 4 0.75 3.83
0.10 0.051 4 6.61
0.25 0.106 6 -3.58
0.30 0.241 1 -5.25
0.35 0.284 2 -2.28
0.40 0.342 0 -4.53
20 0.05 0.050 6 1.29 3.80
0.10 0.103 4 3.45
0.25 0.241 1 -3.57
0.30 0.283 1 -5.64
0.35 0.337 8 -3.49
0.40 0.378 5 -5.36
15 0.05 0.037 1 -25.84 7.33
0.10 0.096 3 -3.69
0.25 0.242 1 -3.15
0.30 0.284 0 -5.34
0.35 0.346 5 -1.00
0.40 0.380 2 -4.95
10 0.05 0.051 2 2.31 3.45
0.10 0.103 2 3.23
0.25 0.244 4 -2.26
0.30 0.281 5 -6.16
0.35 0.349 5 -0.14
0.40 0.373 6 -6.60

表5

最小值模型验证结果"

蓝光亮
度等级		 染料质量浓度/(g·L-1) 相对
误差/%		 平均相对误差
绝对值/%
实际值 预测值
50 0.05 0.047 4 -5.20 3.35
0.10 0.104 1 4.10
0.25 0.245 3 -1.88
0.30 0.290 3 -3.23
0.35 0.342 8 -2.06
0.40 0.385 6 -3.60
20 0.05 0.047 4 -5.20 3.71
0.10 0.108 4 8.40
0.25 0.244 8 -2.08
0.30 0.290 8 -3.07
0.35 0.347 1 -0.83
0.40 0.389 2 2.70
15 0.05 0.041 6 -16.80 6.39
0.10 0.095 7 -4.30
0.25 0.238 0 -4.80
0.30 0.280 4 -6.53
0.35 0.344 3 -1.63
0.40 0.382 9 -4.95
10 0.05 0.045 7 -8.60 3.98
0.10 0.102 0 2.00
0.25 0.258 3 3.32
0.30 0.301 2 0.40
0.35 0.376 8 7.66
0.40 0.407 5 1.87
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