摘要
研究了BP网络、LVQ1网络、LVQ2网络所构建的分类器的性能,将这3种分类器用于中厚板表面缺陷的自动分类中。从现场在线采集中厚板的表面缺陷图像,将每幅表面图像划分成64×64大小的子图像,对子图像进行FFT变换,得到子图像的幅值谱。将幅值谱中心区域内的像素灰度值作为特征量,分别输入给BP网络、LVQ1网络、LVQ2网络所构建的分类器模型,试验表明LVQ2网络能够得到理想的分类效果。
Classifiers based on BP（back propagation） networks, LVQ1 （learning vector quantization） networks and LVQ2 networks were studied and applied to classification of surface defects of plates. Surface defect images of plates were online captured from production line, and each image was divided into several sub-images of size 64 × 64. Fast Fourier Transform（FFT） was used to get amplitude spectrums of each sub-image. Gray levels of pixels in central region of each amplitude spectrum were input to classifiers based on BP networks, LVQ1 networks and LVQ2 networks as features. Experiments show that classification results of LVQ2 networks are satisfactory.
出处
《钢铁》
CAS
CSCD
北大核心
2006年第4期47-50,共4页
Iron and Steel
基金
国家"863"计划课题(2001AA339030
2003AA331080)
关键词
中厚板
表面缺陷
表面检测
神经网络
plate
surface defect
surface inspection
neural network
