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作 者:赵哲 董锁拽 潘璐璐 汪军[3] 陈霞[1] ZHAO Zhe;DONG Suozhuai;PAN Lulu;WANG Jun;CHEN Xia(College of Textile,Donghua University,Shanghai 201620,China;Hangzhou Customs Technology Center,Hangzhou 310000,Zhejiang,China;Key Laboratory of Textile Science&Technology Ministry of Education,Donghua University,Shanghai 201620,China)
机构地区:[1]东华大学纺织学院,上海201620 [2]杭州海关技术中心,浙江杭州310000 [3]东华大学纺织面料技术教育部重点实验室,上海201620
出 处:《上海纺织科技》2020年第11期44-47,51,共5页Shanghai Textile Science & Technology
摘 要:目前生丝清洁疵点的检验仍采用人工目测法,易受主观因素的影响。为了使检验结果更加准确客观,在获取生丝黑板清洁疵点标准样照扫描图像的基础上,通过图像处理、提取特征值、根据特征值进行分步判断等步骤,构建了清洁疵点的数字分类方法。首先,对图像进行疵点分块、水平校正、断点连接和去噪的图像处理操作,为特征值的提取做好准备;然后,在所得二值图像的基础上分别提取了区域特征值和距离特征值;最后,构建了基于这两组特征值的数字分类方法。运用本方法对与标准样照尺寸相同的生丝样品进行相同的图像处理与特征值提取,并根据分类方法进行分类。结果表明,此种分类方法得到的疵点分类判定与人工检测结果具有很好的吻合性,说明其具有适用性。The inspection of raw silk cleanness still uses artificial visual inspection at present,and the results are affected by subjective factors easily.In order to make the inspection results more accurate and objective,on the basis of obtaining the scanned image of the standard sample of the raw silk cleanness blackboard,a digital classification method of cleaning defects is constructed through image processing,feature value extraction,and stepwise judgment based on the feature values.Firstly,the image is processed by image segmentation,horizontal correction,break defect connection of the thread and the defect,and area filtering to prepare for the extraction of the feature values.Then,based on the binary images,regional feature values and distance feature values are extracted respectively.Finally,a digital classification method based on these two sets of feature values is constructed.This method is used on the raw silk samples to be tested with the same size as the standard sample.The results show that the classification of the defects obtained by this method is in good agreement with the artificial detection results,which indicates its applicability.
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