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Real-time implementation of fabric defect detection based on variational automatic encoder with structure similarity

机译：基于结构相似性变分自动编码器的结构缺陷检测的实时实现

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摘要

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.
机译：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的重要课题。织物生产中有很多缺陷，很难自动对缺陷进行分类。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培训和提取目标图像的特征。由于不能完全收集大量缺陷样本，我们基于CNN比较了无监督的学习算法，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的训练量大，我们选择了AE和VAECEC网络，然后引​​入了平均结构相似性（MSSIM）作为网络训练损失功能，以提高仅使用图像的LP距离损耗函数的性能亮度比较。完成后，作者使用训练模型从输入和重建图像之间获得来自SSIM剩余贴图的目标缺陷。根据评估结果，我们最终实现了美国NVIDIA CORPORATION的Jetson TX2上的vae的织物缺陷检测系统。优化的算法可以满足项目的实时要求，实现其普及和应用。

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