浙江农林大学学报2011，Vol.28Issue(6)：937-942,6.
一种基于混合纹理特征的木板材表面缺陷检测方法
A method for wood surface defect detection based on mixed texture features
摘要
Abstract
It is important to detect the wood surface defects using computer vision technology. In this paper, a defect detection method which can accurately and robustly determine whether there is defect on wood surface image or not is proposed based on mixed texture features. At first, gray level co-occurrence matrix (GLCM), Gabor filtering and invariant moment method are used to extract 10 image scale, translation, rotation invariant and texture features optimally. Then, feature vectors are mixed effectively. Finally, BP artificial neural network is used to train the sample sets and detection based on the mixed texture features. Experiments show that the proposed method can detect surface defects of wood boards accurately and the average success rate of detection is 96.2%. [Ch, 4 fig. 1 tab. 12 ref.]关键词
林业工程/灰度共生矩阵/Gabor滤波/不变矩/木板材/缺陷检测Key words
forest engineering/ gray level co-occurrence matrix (GLCM)/ gabor filter/ invariant moment/wood image/ defect detection分类
农业科技引用本文复制引用
尹建新,祁亨年,冯海林,杜晓晨..一种基于混合纹理特征的木板材表面缺陷检测方法[J].浙江农林大学学报,2011,28(6):937-942,6.基金项目
国家自然科学基金资助项目(60970082,60903144) （60970082,60903144）
浙江省自然科学基金资助项目(Y1080777,Y3080457) （Y1080777,Y3080457）
浙江农林大学预研项目(2008KF61,2451005041) （2008KF61,2451005041）
