Automatic detection of fabric defects based on machine vision is an important topic in the quality control of cotton textile factories. There are many kinds of defects in fabric production, it is very difficult to classify the defects automatically. In recent years, deep learning image processing technology based on a convolutional neural network (CNN) can train and extract features of the target image automatically. Since a large number of defect samples cannot be collected completely, we compared unsupervised learning algorithms based on CNN, including auto encoder (AE), variational automatic encoder (VAE), and generative adversarial networks (GAN). Because of the large amount of calculation and the difficulty of training in GAN, we chose AE and VAE codec networks and then introduced mean structural similarity (MSSIM) as network training loss function to improve the performance that only used \({L}_{p}\)-distance loss function for image brightness comparison. After training finished, the authors used the trained model to obtain target defects from SSIM residual maps between input and reconstruct images. According to the evaluation results, we finally implemented a fabric defect detection system based on VAE on Jetson TX2 from Nvidia Corporation, USA. The optimized algorithm can meet the real-time requirements of the project and realize its popularization and application.

基于机器视觉的织物缺陷自动检测是棉纺织工厂质量控制中的重要课题。织物生产中存在多种缺陷，很难对缺陷进行自动分类。近年来，基于卷积神经网络（CNN）的深度学习图像处理技术可以自动训练和提取目标图像的特征。由于无法完全收集大量缺陷样本，因此我们比较了基于CNN的无监督学习算法，包括自动编码器（AE），变分自动编码器（VAE）和生成对抗网络（GAN）。由于GAN的计算量大且训练困难，\（{L} _ {p} \）-距离损失功能，用于图像亮度比较。训练完成后，作者使用训练后的模型从输入和重建图像之间的SSIM残差图获得目标缺陷。根据评估结果，我们最终在美国Nvidia公司的Jetson TX2上实现了基于VAE的织物缺陷检测系统。优化后的算法可以满足项目的实时性要求，可以实现其推广应用。