基于稀疏优化的纺织品激光印花图像缺陷识别研究

Research on defect recognition of textile laser printing image based on sparse optimization

通过对纺织品激光印花图像的缺陷识别,提高对纺织品印花质量检测能力,提出基于稀疏优化的纺织品激光印花图像缺陷识别方法,采用不同的像素块大小特征匹配方法实现对纺织品激光印花的瑕疵检测及显著图参数分析。采用纹理特征匹配方法实现对纺织品印花花边特征点提取与特征匹配处理,根据棉线与棉线间的纹理分布差异性,建立纺织品激光印花的稀疏化特征匹配特征检测模型,根据印花花边自身与生产的视觉特征表达能力,结合位置、尺度、旋转不变量的特征匹配结果实现对纺织品激光印花图像的缺陷识别和检测。测试结果表明,采用该方法进行纺织品激光印花图像缺陷识别的特征匹配能力较好,对缺陷部位的动态检测能力较强,具有很好的图像伪特征点筛出和特征检测能力。

By identifying defects in textile laser printing images,the ability to detect the quality of textile printing is improved.A sparse optimization based method for identifying defects in textile laser printing images is proposed.Different pixel block size feature matching methods are used to achieve defect detection and saliency parameter analysis in textile laser printing.Using texture feature matching method to extract and match feature points of textile printing lace,a sparse feature matching feature detection model for textile laser printing is established based on the difference in texture distribution between cotton threads.Based on the visual feature expression ability of printing lace itself and production,combined with position,scale The feature matching result of rotation invariant realizes defect recognition and detection of textile laser printing image.The test results show that the feature matching ability of using this method for defect recognition in textile laser printing images is good,and the dynamic detection ability of defect parts is strong.It has good ability to screen out false feature points in images and detect features.