Defect detection of zipper tape based on lightweight deep learning network-Reference-Cited by-同舟云学术

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Defect detection of zipper tape based on lightweight deep learning network

Published:2024-08-20 Issue: Volume: Page:1-9
ISSN:0040-5000
Container-title:The Journal of The Textile Institute
language:en
Short-container-title:The Journal of The Textile Institute

Author:

Gu Songwei¹,Li Qiang¹,Zhang Yongju²,Zhang Li²,Wang Ziyan³

Affiliation:

1. College of Mechanical and Energy Engineering, Zhejiang University of Science and Technology, Hangzhou, China

2. Intelligent Manufacturing College, Taizhou University, Taizhou, China

3. Zhejiang Weixing Industrial Development Co., Ltd, Linhai, China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/00405000.2024.2392893

Reference23 articles.

1. Automatic knee meniscus tear detection and orientation classification with Mask-RCNN

2. Automatic zipper tape defect detection using two-stage multi-scale convolutional networks

3. Howard A. G. Zhu M. Chen B. Kalenichenko D. Wang W. Weyand T. Andreetto M. & Adam H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (arXiv:170404861arXiv). http://arxiv.org/abs/1704.04861

4. RPDNet: Automatic Fabric Defect Detection Based on a Convolutional Neural Network and Repeated Pattern Analysis

5. Fabric defect detection based on deep-handcrafted feature and weighted low-rank matrix representation

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