[1]李叶妮,林晓佳,陈吉鹏,等.基于灰度共生矩和SVM的碳化竹条瑕疵识别[J].集美大学学报(自然版),2017,22(3):49-54.
 LI Yeni,LIN Xiaojia,CHEN Jipeng,et al.Defects Identification for Carbonization Bamboos Based on GLCM and SVM[J].Journal of Jimei University,2017,22(3):49-54.
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基于灰度共生矩和SVM的碳化竹条瑕疵识别()
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《集美大学学报(自然版)》[ISSN:1007-7405/CN:35-1186/N]

卷:
第22卷
期数:
2017年第3期
页码:
49-54
栏目:
船舶与机械工程
出版日期:
2017-05-28

文章信息/Info

Title:
Defects Identification for Carbonization Bamboos Based on GLCM and SVM
作者:
李叶妮12林晓佳1陈吉鹏1陈水宣1
(1.厦门理工学院机械与汽车工程学院,福建 厦门 361024;2.厦门大学航空航天学院,福建 厦门 361005)
Author(s):
LI Yeni12LIN Xiaojia1CHEN Jipeng1CHEN Shuixuan1
(1.School of Mechanical and Automotive Engineering,Xiamen University of Technology,Xiamen 361024,China;2.School of Aerospace Engineering,Xiamen University,Xiamen 361005,China)
关键词:
碳化竹条瑕疵识别灰度共生矩SVM
Keywords:
carbonization bamboodefect recognitionGLCMSVM
文献标志码:
A
摘要:
针对碳化竹条瑕疵种类多,位置不确定且竹纹理干扰的问题,利用图像处理技术对竹条图像进行阈值分割,图像滤波等预处理,得到特征明显的图像,从而实现其表面瑕疵特征的识别。通过计算确定灰度共生矩阵的三个构造因子,提取了图像的三个纹理特征,采用一对一淘汰策略的多类SVM(support vector machine)学习模型进行分类识别竹条的瑕疵类型。实验结果表明,该方法对于碳化竹条的黑结、虫蛀、染色、霉点、裂痕等缺陷的正确识别率达到90.6%以上。
Abstract:
In observation of realization of the surface flaw feature recognition,the bamboo original image was pretreated by image graying,Otsu threshold and mean filtering by taking into account of the effect from defects of carbonized bamboo and location uncertainties.Influences from various building factors on GLCM(gray level co-occurrence matrix) and its parameters were invetigated,and the method for establishment of GLCM suitable for describing bamboo surface texture was presented.A class of SVM learning model with one to one replacement strategy was employed to classify and identify the defect types of bamboo.Result shows that the method can classify the five common types of the bamboo defects which includs black node,wormhole,dyeing,mildew and crack with an accuracy higher than 90.6%.
更新日期/Last Update: 2017-09-10