基于全参考彩色图像质量算法的磨削表面粗糙度测量  

Measurement of grinding surface roughness based on the full-reference color image quality algorithm

作　　者：路恩会 郭耀村 李世雯 刘坚[2] 朱兴龙[1] Lu Enhui;Guo Yaocun;Li Shiwen;Liu Jian;Zhu Xinglong(College of Mechanical Engineering,Yangzhou University,Yangzhou 225127,China;State Key Laboratory of Advanced Design and Manufacture for Vehicle Body,Hunan University,Changsha 410082,China)

机构地区：[1]扬州大学机械工程学院,扬州225127 [2]湖南大学汽车车身先进设计制造国家重点实验室,长沙410082

出　　处：《仪器仪表学报》2023年第7期252-259,共8页Chinese Journal of Scientific Instrument

基　　金：科技部中日合作专项(2017YFE0128400);扬州市市校合作专项(YZ2022184)资助。

摘　　要：当前基于机器视觉的粗糙度测量方法使用的图像指标考虑因素少、无参考使得预测模型精度受限、受光源亮度影响大。针对此问题,论文提出了一种基于全参考彩色图像质量算法的粗糙度测量方法。该方法根据粗糙表面成像机理分析,在视觉显著性图像质量评估算法(VSI)的基础上引入结构信息,提出了基于视觉结构显著性(VSIS)的图像质量评估算法,与此同时设计了一套基于图像质量的磨削样块表面粗糙度测量装置。实验结果表明,提出的VSIS图像质量评估算法与表面粗糙度Ra之间存在明显的相关性。使用最小二乘法拟合出的曲线关系,能够对粗糙度大于等于0.965μm的磨削样块进行低离散高精度预测,平均误差为0.111μm,标准差为0.079μm。相比于考虑单一因素的粗糙度关联图像特征指标,VSIS有着较好的综合性能,且能一定程度上克服光源亮度的影响,为非接触粗糙度测量提供可选择途径。The current machine vision-based roughness measurement methods use image metrics with few considerations,no reference making the prediction model accuracy limited and highly influenced by the brightness of the light source.To address this problem,the article proposes a roughness measurement method based on a full-reference color image quality algorithm.Based on the analysis of rough surface imaging mechanism,this method introduces structural information based on visual saliency-induced index(VSI)and proposes an image quality assessment algorithm based on visual saliency structural index(VSIS).Meanwhile,a surface roughness measurement device for grinding samples based on the image quality assessment algorithm is designed.The experimental results show that the proposed VSIS image quality assessment algorithm has a significant correlation with surface roughness(R_a).The curve relationship,obtained through the least squares method,enables low-discrepancy and high-precision predictions for grinding samples with a roughness of 0.965μm or greater.The average error and standard deviation for these predictions are measured at 0.111μm and 0.079μm,respectively.Compared with the roughness-correlated image characteristic index considering a single factor,VSIS has a better comprehensive performance and can overcome the influence of light source brightness to a certain extent.The method provides an alternative way for non-contact roughness measurement.

关 键 词：机器视觉 粗糙度测量 图像特征指标 预测模型 鲁棒性 图像质量 

分 类 号：TN911.73[电子电信—通信与信息系统] TP216.1[电子电信—信息与通信工程] TH701[自动化与计算机技术—检测技术与自动化装置]