摘要
为了对运输导轨上筒子纱的纱管品种进行检测,提出一种基于纱管图像分类的品种检测方法。首先通过搭建的采集装置采集筒子纱顶部包含纱管信息的图像,运用阈值分割和椭圆拟合得到纱管区域,利用极坐标变换将纱管圆环展开成矩形图像,然后使用HSV颜色直方图和局部二值模式分别提取纱管展开图像的颜色特征和纹理特征,最后通过支持向量机构建筒子纱纱管品种分类模型实现纱管品种检测。采用建立的纱管品种检测分类数据集进行实验,结果表明,本文方法相比于其它特征组合和分类器,具有更高的分类准确率,对相同图案的星型纱管、黑色系花纹纱管和混合纱管的分类准确率达100%,可为纺纱企业筒子纱纱管品种检测与运输包装提供参考。
Objective Aiming at variety detection of cheese yarn on the transport guide rail in practical situations,this paper proposed an automatic solution based on image processing.Based on the characteristics of bobbin,a yarn variety detection method based on bobbin image classification was proposed to replace subjective judgment.The method proposed in this paper is aimed to reduce error rate of manual detection and labor costs,and to improve the production efficiency of spinning.Method In order to facilitate feature extraction,the original image of cheese yarn was processed by the image segmentation method to obtain the bobbin area.Then,the segmented image of the annular bobbin was expanded into a square image by polar coordinate transformation.Color and texture features were extracted from the expanded image and optimized based on feature classification experiments and the elapsed time to jointly characterize the bobbin image.Results The Otsu threshold method was adopted to find the gray threshold of the bobbin foreground and background,and a binary image was obtained based on the determined threshold.The image contour was used to filter non-target regions in the binary image by setting the area and perimeter threshold of the bobbin region.The binary image containing only the region of the bobbin was adopted as a mask to segment the yarn tube from the original cheese yarn image.For the segmented annular yarn tube,the polar coordinate transformation was applied to transform it into a rectangular image with the circumference of the outer circle and the width of the outer circle.Bobbin image expanded after threshold segmentation provided data support for subsequent research.The non-uniform quantized color histogram features with H:S:V=8:3:3 were optimized by the classification accuracy and the elapsed time of feature extraction(Tab.1).The features obtained by the preferred color quantization method demonstrated satisfactory classification effect on different types of bobbin images with the acceptable calculation time.The
作者
马传旭
张宁
潘如如
MA Chuanxu;ZHANG Ning;PAN Ruru(Key Laboratory of Eco-Textiles(Jiangnan University),Ministry of Education,Wuxi,Jiangsu 214122,China)
出处
《纺织学报》
EI
CAS
CSCD
北大核心
2023年第1期194-200,共7页
Journal of Textile Research
基金
国家自然科学基金项目(61976105)
中国纺织工业联合会基础研究项目(J202006)。
关键词
筒子纱纱管
支持向量机
极坐标变换
特征提取
图像分类
品种检测
bobbin
support vector machine
polar coordinate transformation
feature extraction
image classification
variety detection
