家电科技 ›› 2025, Vol. 0 ›› Issue (2): 66-71.doi: 10.19784/j.cnki.issn1672-0172.2025.02.009

• 论文 • 上一篇    下一篇

基于深度卷积神经网络的智能衣物识别方法

卞国龙1,2, 高菲菲1,2, 郝世龙1,2, 张永强1,2   

  1. 1.青岛海尔智能技术研发有限公司 山东青岛 266101;
    2.数字家庭网络国家工程研究中心 山东青岛 266101
  • 出版日期:2025-04-01 发布日期:2025-07-10
  • 作者简介:卞国龙,机械工程硕士学位。研究方向:衣物护理技术。Email:bianguolong@haier.com。

Intelligent clothing recognition method based on deep convolutional neural network

BIAN Guolong1,2, GAO Feifei1,2, HAO Shilong1,2, ZHANG Yongqiang1,2   

  1. 1. Qingdao Haier Smart Technology R&D Co., Ltd. Qingdao 266101;
    2. National Engineering Research Center of Digital Home Networking Qingdao 266101
  • Online:2025-04-01 Published:2025-07-10

摘要: 随着高净值人群的增长,衣物的传统洗护方法已无法满足人们的品质追求,智能洗护技术可通过精确识别衣物材质从而智能匹配洗护程序,满足高品质洗护需求。建立了一种基于深度卷积神经网络图像识别的衣物材质识别方法,对衣物类型识别后再进一步对衣物材质识别,从而实现快速、精准的材质识别与衣物分类。根据测试脚本对衣物护理柜内实拍的4044张图片数据进行测试,结果表明衣物类型分类准确率为92.06%,材质分类准确率为94.00%,同时识别时准确率为87.27%,无衣物识别准确率为100.00%。该衣物材质识别方法准确率高且泛化能力强,可为智能衣物洗护程序提供技术支持,具有广阔的应用前景。

关键词: 材质识别, 深度卷积神经网络, 智能衣物洗护

Abstract: With the growth of the high-net-worth population, traditional clothing care methods are no longer able to meet people's pursuit of quality. Intelligent clothing care technology can intelligently match care programs by accurately identifying clothing materials, satisfying the demand for high-quality care. This article establishes a clothing material recognition method based on deep convolutional neural network image recognition. It first recognizes the type of clothing and then further identifies the material of the clothing, achieving fast and accurate material recognition and clothing classification. Testing was conducted on over 4000 images of clothing taken in a clothing care cabinet according to the test script. The results show an accuracy of 92.06% in clothing type classification, 94.00% in material classification, 87.27% in identification accuracy, and 100.00% accuracy in cases with no clothing identified. This clothing material recognition method exhibits high accuracy and strong generalization ability, can providing technical support for intelligent clothing care programs, possessing broad application prospects.

Key words: Material recognition, Deep convolutional neural network, Intelligent clothing care

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