人工智能
深度学习
计算机科学
阶段(地层学)
模式识别(心理学)
污渍
对偶(语法数字)
材料科学
地质学
医学
染色
艺术
文学类
病理
古生物学
作者
Yuming Zhang,Zhongyuan Gao,Chao Zhi,Mengqi Chen,Youyong Zhou,Shuai Wang,Sida Fu,Lingjie Yu
出处
期刊:E-polymers
[De Gruyter]
日期:2022-01-01
卷期号:22 (1): 793-802
被引量:2
标识
DOI:10.1515/epoly-2022-0071
摘要
Abstract The fabric defect models based on deep learning often demand numerous training samples to achieve high accuracy. However, obtaining a complete dataset containing all possible fabric textures and defects is a big challenge due to the sophisticated and various fabric textures and defect forms. This study created a two-stage deep pix2pixGAN network called Dual Deep pix2pixGAN Network (DPGAN) to address the above problem. The defect generation model was trained based on the DPGAN network to automatically “transfer” defects from defected fabric images to clean, defect-free fabric images, thus strengthening the training data. To evaluate the effectiveness of the defect generation model, extensive comparative experiments were conducted to assess the performance of the fabric defect detection before and after data enhancement. The results indicate that the detection accuracy was improved regarding the belt_yarn, hole, and stain defect.
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