Yarn-dyed Fabric Defect Detection with YOLOV2 Based on Deep Convolution Neural Networks-Reference-Cited by-同舟云学术

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Yarn-dyed Fabric Defect Detection with YOLOV2 Based on Deep Convolution Neural Networks

Published:2018-05 Issue: Volume: Page:
ISSN:
Container-title:2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)
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Author:

Zhang Hong-wei,Zhang Ling-jie,Li Peng-fei,Gu De

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8479537/8515899/08516094.pdf?arnumber=8516094

Cited by 58 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An approach to automatic fault detection in four-point system for knitted fabric with our benchmark dataset Isl-Knit;Heliyon;2024-09

2. Fabric defects identification for textile industry with a deep learning approach;The Journal of The Textile Institute;2024-08-05

3. Real-time detection of plastic part surface defects using deep learning- based object detection model;Measurement;2024-08

4. Optimized Fabric Imperfection Detection via Image Analysis and Isolation Forest Technique;2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT);2024-07-04

5. Research on Fabric Defect Detection Algorithm Based on Lightweight YOLOv7-Tiny;Journal of Natural Fibers;2024-06-12

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