Journal ArticleDOI
Deformable Patterned Fabric Defect Detection With Fisher Criterion-Based Deep Learning
Reads0
Chats0
TLDR
Experimental results demonstrate the effectiveness of the proposed scheme in the defect detection for periodic patterned fabric and more complex jacquard warp-knitted fabric.Abstract:
In this paper, we propose a discriminative representation for patterned fabric defect detection when only limited negative samples are available. Fabric patches are efficiently classified into defectless and defective categories by Fisher criterion-based stacked denoising autoencoders (FCSDA). First, fabric images are divided into patches of the same size, and both defective and defectless samples are utilized to train FCSDA. Second, test patches are classified through FCSDA into defective and defectless categories. Finally, the residual between the reconstructed image and defective patch is calculated, and the defect is located by thresholding. Experimental results demonstrate the effectiveness of the proposed scheme in the defect detection for periodic patterned fabric and more complex jacquard warp-knitted fabric.read more
Citations
More filters
Journal ArticleDOI
Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks
TL;DR: This paper discusses the automatic detection of metallic defects with a twofold procedure that accurately localizes and classifies defects appearing in input images captured from real industrial environments using a novel cascaded autoencoder (CASAE) architecture.
Journal ArticleDOI
An Unsupervised-Learning-Based Approach for Automated Defect Inspection on Textured Surfaces
Shuang Mei,Hua Yang,Zhouping Yin +2 more
TL;DR: This paper proposes an approach to detect and localize defects with only defect-free samples for model training by reconstructing image patches with convolutional denoising autoencoder networks at different Gaussian pyramid levels, and synthesizing detection results from these different resolution channels.
Journal ArticleDOI
Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model
Shuang Mei,Yudan Wang,Guojun Wen +2 more
TL;DR: This paper proposes an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention, used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels.
Journal ArticleDOI
Visual-Based Defect Detection and Classification Approaches for Industrial Applications-A SURVEY.
Tamas Czimmermann,Gastone Ciuti,Mario Milazzo,Marcello Chiurazzi,Stefano Roccella,Calogero Maria Oddo,Paolo Dario +6 more
TL;DR: This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles, and describes artificial visual processing techniques that are aimed at understanding of the captured scenery in a mathematical/logical way.
Journal ArticleDOI
Fabric defect detection systems and methods—A systematic literature review
TL;DR: A comprehensive literature review of fabric defect detection methods, categorized into seven classes as structural, statistical, spectral, model-based, learning, hybrid and comparison studies, finds weaknesses of each approach.
References
More filters
Journal ArticleDOI
Reducing the Dimensionality of Data with Neural Networks
TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.
Journal ArticleDOI
A fast learning algorithm for deep belief nets
TL;DR: A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.
Proceedings ArticleDOI
Extracting and composing robust features with denoising autoencoders
TL;DR: This work introduces and motivate a new training principle for unsupervised learning of a representation based on the idea of making the learned representations robust to partial corruption of the input pattern.
Book ChapterDOI
A Practical Guide to Training Restricted Boltzmann Machines
TL;DR: This guide is an attempt to share expertise at training restricted Boltzmann machines with other machine learning researchers.
Posted Content
Practical recommendations for gradient-based training of deep architectures
TL;DR: Overall, this chapter describes elements of the practice used to successfully and efficiently train and debug large-scale and often deep multi-layer neural networks and closes with open questions about the training difficulties observed with deeper architectures.