欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
针对带钢表面缺陷的识别和分类技术，本文采用一种将不变矩与主成分分析法相结合的特征提取方法。首先，对每幅缺陷图像提取22维不变矩特征向量，满足对图像平移、尺度及旋转变化都不敏感；然后，为了提高分类器的效率，应用主成分分析法对特征向量进行空间降维处理，得到4维特征向量；最后，将特征向量作为BP神经网络的输入，对网络进行权值和阈值训练，达到缺陷分类的目的。实验结果表明，该方法对带钢表面缺陷的平均正确识别率可达到85％以上。
英文摘要：
A method of feature extraction which is composed of invariable moment functions and Principal Component Analysis （PCA） is presented in order to recognize and classify the surface defects of strips. First, a 22-dimensional eigenvector which was invariable was extracted from images when the image was translated, scaled and rotated. And then, in order to improve the efficiency of classification, PCA was applied to reduce the dimension of the eigenvector. As a result, the 4-dimensional eigenvector was obtained. Finally, using these eigenvectors as input, weights and thresholds of the BP neural network were trained for the purpose of defect classification. Experimental results show that the average efficiency of the correct identification can reach 85%, and it＇s fit for the application for detection of surface defects of strips,
同期刊论文项目
同项目期刊论文
期刊信息
位置：