基于HSV(色相-饱和度-明度)与形状特征的涂层锈点图像识别*

刘佳; 唐鋆磊; 林冰; 王丹; 郑宏鹏; 王莹莹; 李平; 钟文胜

引用本文:	刘佳,唐鋆磊,林冰,王丹,郑宏鹏,王莹莹,李平,钟文胜.基于HSV(色相-饱和度-明度)与形状特征的涂层锈点图像识别*[J].中国表面工程,2023,36(4):217~228
	LIU Jia,TANG Junlei,LIN Bing,WANG Dan,ZHENG Hongpeng,WANG Yingying,LI Ping,ZHONG Wensheng.Rust Spot Image Recognition of Coatings Based on HSV and Shape Feature[J].China Surface Engineering,2023,36(4):217~228

【打印本页】【HTML】【下载PDF全文】【查看/发表评论】【EndNote】【RefMan】【BibTex】

←前一篇|后一篇→

过刊浏览高级检索

本文已被：浏览 867次下载 582次	码上扫一扫！
分享到：微信更多字体:加大+\|默认\|缩小-
基于HSV(色相-饱和度-明度)与形状特征的涂层锈点图像识别*
刘佳¹, 唐鋆磊¹, 林冰¹, 王丹^1,2, 郑宏鹏¹, 王莹莹³, 李平⁴, 钟文胜⁵
1.西南石油大学化学化工学院成都 610500;2.常熟理工学院电气与自动化工程学院常熟 215500;3.江汉大学光电化学材料与器件（教育部）重点实验室武汉 430021;4.西南石油大学计算机科学学院成都 610500;5.四川航启科技发展有限公司彭州 611900

摘要:

图像识别技术广泛应用于涂层领域，图像特征的选择是提升识别率的重要因素，而形状特征在涂层锈点的图像识别中未见报道。基于涂层锈点的颜色和形状特征，结合机器学习对其进行图像识别。通过采集 3 种常见自然光照强度下的 90 张涂层锈点图像，使用同态滤波对图像进行预处理，利用 HSV（色相-饱和度-明度）颜色空间来区分锈点与无锈点区域。然后提取锈点的 8 种形状特征对锈点区域进一步细化，用 Pearson 相关系数对形状特征进行筛选，将颜色特征、单一形状特征、8 种组合形状特征、筛选后的组合形状特征、颜色特征与筛选后组合形状特征的融合特征分别作为参量输入 Linear 核函数、RBF 核函数、Polynomial 核函数和 Sigmoid 核函数 4 种核函数的支持向量机（SVM）对锈点进行识别。研究结果表明：联合 SVM 与颜色、形状特征参量构建的图像识别算法能较准确地识别涂层锈点，其中基于颜色特征与筛选后形状特征的融合特征的准确识别率最高可达 93.33%。形状特征可作为另一种特征信息来提高锈点图像识别的精确度，可为涂层锈点的图像识别技术研究提供参考依据。

关键词: 涂层锈点形状特征支持向量机图像处理机器学习

DOI：10.11933/j.issn.1007?9289.20221008001

分类号:TG156；TB114

基金项目:四川省重大科技专项（2021ZDZX0002）；四川省科技计划（2021YFSY0055）；辽宁省 2021 年博士科研基金（2021-BS-058）资助项目

Rust Spot Image Recognition of Coatings Based on HSV and Shape Feature

LIU Jia¹, TANG Junlei¹, LIN Bing¹, WANG Dan^1,2, ZHENG Hongpeng¹, WANG Yingying³, LI Ping⁴, ZHONG Wensheng⁵

1.School of Chemistry and Chemical Engineering, Southwest Petroleum University, Chengdu 610500 , China;2.School of Electrical and Automation Engineering, Changshu Institute of Technology, Changshu 215500 , China;3.Key Laboratory of Optoelectronic Chemical Materials and Devices (Ministry of Education),Jianghan University, Wuhan 430021 , China;4.School of Computer Science, Southwest Petroleum University, Chengdu 610500 , China;5.Sichuan Hangqi Technology Development Co, Ltd., Pengzhou 611900 , China

Abstract:

Image recognition technology is used extensively in various industrial fields, and rust spots are a form of commonly occurring damage to coatings in service. Further, the quality inspection of coating surfaces relies primarily on manual work, which is limited by its subjectivity. Therefore, based on machine learning and image processing technology, a recognition method for coating rust spots is proposed that includes a complete set of recognition systems for rust spots from image acquisition, homomorphic filtering preprocessing, feature extraction and screening, and support vector machine(SVM) judgment recognition. Because the coating aging failure is usually detected outdoors, 90 images of coating rust spots under three common natural light intensities, namely sunny days with smooth light, sunny days with backlight, and cloudy days, were collected in this study. Among them, 60 were randomly selected as the test set and the remaining 30 were used as the training set. First, the images were pre-processed using homomorphic filtering to reduce the effect of illumination on the image quality and to facilitate accurate feature extraction. Subsequently, the hue, saturation and value(HSV) color space was used to distinguish the rusted and not-rusted areas which were identified by controlling the value of the H component. Additionally, the eight shape features of the rusted areas were extracted to further refine the rusted areas: rusted area (S), rusted perimeter (L), rusted area (L), rusted area (L), and rusted area (L). These eight shape features were the rust spot area (S), perimeter (L), minimum external rectangle and minimum external ellipse (S_c), roundness (?₁), complexity (?₂), elongation (?₃), compactness (?₄), and area concavity ratio (?₅). As the number of images in this paper was small and the number of feature parameters was large, it was easy to produce overfitting, thus, the shape features were filtered using the Pearson correlation coefficient, and the area (S), perimeter (L), complexity (?₂), and area-concave ratio (?₅). Finally, the color features, single shape features, eight combined shape features, screened combined shape features, and fused features of color features and screened combined shape features were input as parameters into the linear kernel function, radials basis function, polynomial kernel function, and sigmoid kernel function, respectively. An SVM with these four kernel functions was used to identify the rusty points. The results showed that the correct recognition rate of rust spots by the SVM based on single-shape features was lower than that based on combined shape features, indicating that the single-shape features cannot accurately describe the rust spots. The correct recognition rate of the four kernel functions based on the four screened shape features was higher than that of the eight unscreened shape features, indicating that the screening of shape features using Pearson's correlation coefficient was effective. The correct recognition rate of the fusion features based on the color features and screened shape features was further improved, and its average correct recognition rate was 89%, among which the SVM recognition of the Linear kernel function was the greatest, and the correct recognition rate reached 93.33%, indicating that the algorithm based on the fusion features could significantly reduce the respective interference factors of the color and shape in the case of the small samples. The SVM can be developed into a classifier that can be used to identify the rust spots on the coating surface. The shape features of the rust spots can provide other feature information to improve the accuracy of image recognition. Further, the machine learning algorithm of the color and shape fusion features is effective and can identify rust spots quickly and accurately compared with the traditional color-based algorithm.

Key words: coating rust spot shape characteristics support vector machine image processing machine learning