森林工程2017，Vol.33Issue(4)：40-43,54,5.
基于灰度共生矩阵和模糊BP神经网络的木材缺陷识别
Wood Defects Recognition Based on Gray-level Co-occurrence Matrix and Fuzzy BP Neural Network
摘要
Abstract
It is important to enhance the accuracy in wood defects detection against the serious shortage of wood resources situation.Wood defects images were acquired by X-ray nondestructive testing technology.Feature vector which was the major characteristics of images could be effectively extracted by gray level co-occurrence matrix.At the same time,the fuzzy BP neural network(FBP)was designed by the combination of fuzzy mathematics and BP neural network.The maximum membership degree principle was used to do the pattern recognition of feature vectors,and then the automatic recognition and classification of wood defects could be realized.After a lot of training,results showed that the average recognition rate of FBP is above 90％.Therefore,FBP has a high recognition accuracy for wood defects,which can provide an important theoretical basis for defects identification.关键词
木材缺陷/灰度共生矩阵/特征提取/模糊BP神经网络Key words
Wood defects/GLCM/feature extraction/fuzzy BP neural network分类
农业科技引用本文复制引用
牟洪波,王世伟,戚大伟,倪海明..基于灰度共生矩阵和模糊BP神经网络的木材缺陷识别[J].森林工程,2017,33(4):40-43,54,5.基金项目
国家自然科学基金项目(31570712) （31570712）
黑龙江省自然科学基金项目(C201338) （C201338）
