摘要
针对缺陷图像表面复杂多变、特征不宜提取的特点,提出了一种归一化转动惯量特征和不变矩特征相结合的时域分析方法来构建缺陷图像的统计特征量,同时增加缺陷矩形框区域内压缩度、距离极值比和线度特征量作为缺陷分类的依据;提出了在缺陷频谱图像内提取特征量的频域分析方法,并将矩形框区域内所有像素点灰度平均值和灰度方差值作为缺陷分类的另一重要依据;同时将BP神经网络应用于缺陷图像的自动分类中,构建了系统的缺陷分类器,并对现场采集的常见6种缺陷类型进行了实验.结果表明,该特征提取方法在很大程度上提高了特征的分类有效性;该BP分类器识别率较高,现场整体识别率达到90%以上,在一定程度上解决了缺陷图像分类难的问题.
Being aimed at the characteristic in complexity and levity of defect image surface,a novel method combined NMI feature with invariant feature in time domain to conceive the statistic feature of defect images is put forward.Simultaneously,compactness feature,L-S factor feature and linearity feature in the rectangular region are developed as one basis of defect classification.Moreover,in frequency domain,a method which can extract features in the rectangular region of central bright area of defect spectrum image is proposed,and maximum difference and average difference of gray value of all the pixels in this rectangular region are developed as another important basis of defect classification.This paper also applies BP neural network to the automatic classification of defect images,constructs the defect classifier and tests six types of common defects collected from online data.The experimental result shows that the new features extraction method increases the validity of classification of feature greatly and this BP classifier has high identification accuracy and the overall recognition rate is over 90%.This new technique resolves the difficulty of defect classification on defect images to some extent.
出处
《北京工业大学学报》
EI
CAS
CSCD
北大核心
2010年第4期450-457,共8页
Journal of Beijing University of Technology
基金
国家自然科学基金资助项目(60372047)
关键词
缺陷图像
特征提取
缺陷分类
BP神经网络
defect image
feature extraction
defect classification
BP neural network
