Advanced Textile Technology ›› 2022, Vol. 30 ›› Issue (2): 36-40.DOI: 10.19398/j.att.202104002

• Testing and Analysi • Previous Articles     Next Articles

Recognition technology of cashmere and wool fibers based on mask R-CNN deep learning

CONG Mingfang1, LI Ziyin2, LU Yang1(), HAN Gaofeng1, XIE Lingjia1, WANG Qizhen2   

  1. 1. Zhejiang Light Industrial Products Inspection and Research Institute, Hangzhou 310018, China
    2. College of Optical and Electronic Technology, China Jiliang University, Hangzhou 310018, China
  • Received:2021-04-02 Online:2022-03-10 Published:2021-08-04
  • Contact: LU Yang

基于Mask R-CNN深度学习的羊绒羊毛纤维识别技术

从明芳1, 李子印2, 卢鸯1(), 韩高锋1, 谢凌佳1, 王启真2   

  1. 1.浙江省轻工业品质量检验研究院,杭州 310018
    2.中国计量大学光学与电子科技学院,杭州 310018
  • 通讯作者: 卢鸯
  • 作者简介:从明芳(1986-),女,浙江杭州人,工程师,硕士,主要从事纺织品检测方面的研究。
  • 基金资助:
    国家市场监督管理总局科技计划项目(2019MK031)

Abstract:

To enhance the level of automation for wool fiber quantification, the mask R-CNN deep learning technology was introduced for the processing of the pictures collected by the optical microscope, optimization of algorithm model, learning and training. An automatic recognition model of cashmere and sheep wool was established. Through the verification test using a test set, it was found that the accuracy of automatic recognition of cashmere and sheep wool fibers reached more than 95%, confirming the feasibility of the recognition technology developed in this paper.

Key words: cashmere, sheep wool, image processing, deep learning, mask R-CNN

摘要:

为提高羊绒羊毛纤维定量的自动化程度,引入基于掩模区域卷积神经网络(Mask R-CNN)深度学习技术,对通过光学显微镜采集的图片进行图片处理、算法模型优化,以及学习和训练,建立起山羊绒和绵羊毛的自动识别模型。采用测试集对所建立的模型进行了验证测试,结果表明,对山羊绒和绵羊毛纤维的自动识别正确率达到95%以上,证实了所建立的识别技术的可行性。

关键词: 山羊绒, 绵羊毛, 图像处理, 深度学习, Mask R-CNN

CLC Number: