计算机科学
特征提取
卷积神经网络
生成语法
人工智能
特征(语言学)
模式识别(心理学)
集合(抽象数据类型)
图像(数学)
对抗制
生成对抗网络
深度学习
语言学
哲学
程序设计语言
作者
Jie Liu,Bengong Zhang,Li Li
标识
DOI:10.1109/cac51589.2020.9327368
摘要
Defect feature extraction is mainly problem of detect detection in fabrics. There are many traditional defect detection methods in it. But deep learning shows many advantages in defect feature extraction of fabric. However, with small unpaired dataset, the recognition rate is always not satisfied. To solve this question, we plan to use Generative Adversarial Network (GAN) to train it in this work. Firstly, we use GAN to evaluate the defect feature distribution on the defect image and generate defect blocks. Secondly, we use these patches sampled above to build paired training data sets with the necessary size. And finally, we use Faster Recurrent Convolutional Neural Networks (Faster R-CNN) for further defect detection with the new data set generated in the second step. The experiment proves the superiority of this method in fabric defect detection.
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