基于GA-SVM模型的机采籽棉杂质识别  

Recognition for machine picking seed cotton impurities based on GA-SVM model

作　　者：张成梁[1] 李蕾[2] 董全成[1] 葛荣雨[1] Zhang Chengliang Li Lei Dong Quancheng Ge Rongyu(School of Mechanical Engineering, University of Jinan, Jinan 250022, China School of Mechanical ＆ Automotive Engineering, Qilu University of Technology, Jinan 250353, China)

机构地区：[1]济南大学机械工程学院,济南250022 [2]齐鲁工业大学机械与汽车工程学院,济南250353

出　　处：《农业工程学报》2016年第24期189-196,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(51305164;51405194);山东省重点研发计划项目(2016GNC110025)

摘　　要：针对中国机采棉加工过程中混级混轧、缺乏棉花参数检测的现状,提出使用遗传算法优化支持向量机参数的机采籽棉图像分割、杂质识别方法。在图像分割阶段,采用像素点邻域的色调、饱和度、亮度颜色特征与平均亮度、平均对比度、平滑度、三阶矩、一致性、熵等纹理特征构建特征向量,使用最优保留策略的遗传算法优化惩罚参数及核函数参数,建立图像分割SVM分类器;对杂质识别过程,在计算标记区域的颜色特征、纹理特征基础上,增加面积、周长、离心率、矩形度、形状因子等形状特征,使用遗传算法建立杂质识别SVM分类器。测试结果表明,该方法适用于边缘对比度低、纹理信息丰富的机采籽棉含杂图像分割,对杂质的有效识别率为92.6%。该研究为棉花加工设备的参数优化和国产采棉机的研制及优化提供重要参考依据。The processing technology of the machine picking seed cotton（MPSC） should be influenced significantly by the kinds and the contents of impurities.But during the MPSC processes in China,there is a lot of the mixed level and the mixed ginned,as well as the lack of online detection technology.In view of the present situations,a method for the identification of impurities in MPSC image using genetic algorithm（GA） to optimize the SVM parameters has been presented in this paper.First,in order to label three categories of the cotton fiber,the light-colored impurities and the dark impurities,the feature vectors were constructed for the image segmentation by the color characteristics of hue,saturation and intensity of pixel neighborhood,and the texture features of the average brightness,the average contrast,the smoothness,the third moment,the consistency and the entropy.GA of optimal retention strategy was used to optimize the penalty parameter and kernel function parameter to establish the SVM classifier of image segmentation,and then morphological operation such as hole filling,opening operation,closing operation was adopted.The specific operation was the use of open operation on light-colored impurities to eliminate the influence of small area noise; while the use of closed operation on dark impurities to smooth the target boundary on the basis of retaining small areas of dust.Then,for the impurity recognition process,shape features including area,perimeter,eccentricity,rectangle degree and shape factor were added to feature vectors besides color feature and texture feature of marked region; five categories of cottonseed,cotton leaf,bell shell,stiff valve and dust miscellaneous were marked; and the SVM classifier for impurity recognition by GA was established.At last,automatic segmentation and impurity recognition for MPSC image were realized using these two SVM classifiers.For the experiment MPSC was taken as the test material.The color planar array CCD camera of technical grade（MV-EM510C/M,Microvision,Inc.�

关 键 词：图像分割 杂质 遗传算法 机采籽棉 SVM 识别 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]