Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (14): 116-125.DOI: 10.3778/j.issn.1002-8331.2003-0322

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Deep Learning-Based Crease Detection and Examination of Coated Fabrics

Alimu⋅Anwaier, ZHANG Daxu, HE Wei, CHEN Wujun, WANG Xiaoyan, ZHOU Qunchao, LUO Yidong, CHEN Nengfu, SUN Kunpeng   

  1. 1.School of Naval Architecture Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    2.Shanghai Key Laboratory for Digital Maintenance of Buildings and Infrastructure, Shanghai 200240, China
    3.China Special Aircraft Research Institute, Jingmen, Hubei 448035, China
    4.Space Structures Research Centre, Shanghai Jiao Tong University, Shanghai 200240, China
    5.School of Computer Science & Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China
  • Online:2021-07-15 Published:2021-07-14



  1. 1.上海交通大学 船舶海洋与建筑工程学院,上海 200240
    2.上海市公共建筑和基础设施数字化运维重点实验室,上海 200240
    3.中国特种飞行器研究所,湖北 荆门 448035
    4.上海交通大学 空间结构研究中心,上海 200240
    5.上海应用技术大学 计算机科学与信息工程学院,上海 201418


The coated fabrics introduce crease damage in the process of manufacturing, transportation, inflation and deflation. The manual crease detection is low efficient. The crease detection accuracy of conventional image processing algorithms cannot meet the practical requirements. An end-to-end deep convolutional neural network for crease detection of coated fabrics is proposed. Datasets are established by performing standard flex durability tests. The encoder and decoder adopt a multi-scale feature fusion structure and an optimized up-sampling model, respectively. Real-time statistics of the crease geometric information are computed by image morphology processing. The crease detection accuracy of the proposed method is as high as 95.78%. In comparison with the conventional semantic segmentation method and other deep learning networks, the performance of the crease detection has been greatly improved.

Key words: coated fabrics, automated crease detection, Deep Convolutional Neural Network(DCNN), morphological processing



关键词: 涂层织物, 折皱智能识别, 深度卷积神经网络, 形态学处理