摘要
针对目前日用陶瓷缺陷检测主要依赖人工检测,现有的视觉检测算法中,存在检测缺陷种类单一,对光照强度过于敏感,适应性和通用性比较差的问题,本文以日用陶瓷盘为研究对象,提出了一种基于多尺度特征融合的陶瓷盘缺陷分类算法。首先,使用中值滤波处理,滤除细节噪声,根据训练图像和测试图像的灰度值进行不同模式的线性变换,减小同种缺陷的特征距离,同时还能在一定程度上对光照强度有一定的适应性;其次,计算多尺度灰度直方图统计数据特征;再使用Sobel算子对图像进行梯度处理,计算梯度图像的灰度共生矩阵,并推算得到梯度图像的能量、相关性、均匀性、对比度、熵、同向各向异性6组纹理特征;最后,将灰度直方图统计特征和灰度共生矩阵下的6组纹理特征相融合后,并传入KNN分类器中训练,并得到相应的陶瓷盘缺陷分类模型。实验结果表明,采用该算法对陶瓷盘表面进行缺陷检测,对常见陶瓷盘正反面缺陷的平均识别率达到86.86%,同时兼具实时性和鲁棒性的优点。
At present,the defect of daily-use ceramics is mainly manually detected.In the current visual inspection algorithms,there are various problems,such as limited defect type,high sensitivity to light intensity,poor adaptability and universality.In this paper,a defect classification algorithm based on multi-scale feature fusion was proposed.Firstly,a median filter was used to filter the detail noise.According to the gray scales of the training image and the test image,linear transformations with different modes were carried out to reduce the feature distance of the same defects and increase adaptability to the light intensity to a certain extent meanwhile.Secondly,the statistical data characteristics of multi-scale gray histogram were calculated.Then,the image was processed with the Sobel operator,in order to calculate the gray level co-occurrence matrix of the gradient image,thus resulting energy,correlativity,uniformity,contrast,entropy and isotropy.Finally,the statistical features of the gray histogram and the six groups of texture features in the gray level co-occurrence matrix were fused and then transferred to the KNN classifier for training,after which ceramic disc defect classification model was developed.This method exhibited an average defect recognition rate of 86.86%on the surface of ceramic disk,demonstrating the advantages of real-time and robustness.
作者
林刚
冯浩
曹利钢
潘海鹏
曹旭明
LIN Gang;FENG Hao;CAO Ligang;PAN Haipeng;CAO Xuming(School of Mechanical and Electronic Engineering,Jingdezhen Ceramic Institute,Jingdezhen 333403,Jiangxi,China)
出处
《陶瓷学报》
CAS
北大核心
2021年第1期143-149,共7页
Journal of Ceramics
基金
江西省教育厅科技项目(160899)
江西省2019年度研究生创新专项资金项目(YC2019-S385)。
关键词
线性变换
多尺度灰度统计
自适应性
KNN算法
灰度共生矩阵
linear transformation
multiscale gray statistics
adaptability
KNN algorithm
gray level co-occurrence matrix
