Journal ArticleDOI
LSTM-RNN-based defect classification in honeycomb structures using infrared thermography
Caiqi Hu,Yuxia Duan,Shicai Liu,Yiqian Yan,Ning Tao,Ahmad Osman,Clemente Ibarra-Castanedo,Stefano Sfarra,Dapeng Chen,Cunlin Zhang +9 more
TLDR
This paper proposes an infrared thermography-based NDT technique and a long short term memory recurrent neural network (LSTM-RNN) model which automatically classifies common defects occurring in honeycomb materials, including debonding, adhesive pooling, and liquid ingress.About:
This article is published in Infrared Physics & Technology.The article was published on 2019-11-01. It has received 45 citations till now.read more
Citations
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Journal ArticleDOI
Non-destructive testing and evaluation of composite materials/structures: A state-of-the-art review
TL;DR: In this paper, the authors describe the increasing demands in their applications to improve product efficiency, cost-effectiveness and the development of superior specific properties of composite materials/structures.
Journal ArticleDOI
State of the Art in Defect Detection Based on Machine Vision
TL;DR: A detailed description of the application of deep learning in defect classification, localization and segmentation follows the discussion of traditional defect detection algorithms.
Journal ArticleDOI
Non-destructive testing and evaluation of composite materials/structures: A state-of-the-art review
TL;DR: The most established non-destructive testing techniques for detection and evaluation of defects/damage evolution in composites are reviewed, which include acoustic emission, ultrasonic testing, infrared thermography, terahertz testing, shearography, digital image correlation, as well as X-ray and neutron imaging.
Journal ArticleDOI
Infrared machine vision and infrared thermography with deep learning: A review
TL;DR: The rapid development of deep learning makes IRMV more and more intelligent and highly automated, thus considerably increasing its range of applications, including unmanned vehicles, mobile phones and embedded systems.
Journal ArticleDOI
A Review on Recent Advances in Vision-based Defect Recognition towards Industrial Intelligence
TL;DR: This paper surveys the recent advances in vision-based defect recognition and presents a systematical review from a feature perspective, and divides the recent methods into designed- feature based methods and learned-feature based methods, and summarizes the advantages, disadvantages and application scenarios.
References
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Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Journal ArticleDOI
A systematic analysis of performance measures for classification tasks
Marina Sokolova,Guy Lapalme +1 more
TL;DR: This paper presents a systematic analysis of twenty four performance measures used in the complete spectrum of Machine Learning classification tasks, i.e., binary, multi-class,multi-labelled, and hierarchical, to produce a measure invariance taxonomy with respect to all relevant label distribution changes in a classification problem.
Journal ArticleDOI
A Novel Connectionist System for Unconstrained Handwriting Recognition
TL;DR: This paper proposes an alternative approach based on a novel type of recurrent neural network, specifically designed for sequence labeling tasks where the data is hard to segment and contains long-range bidirectional interdependencies, significantly outperforming a state-of-the-art HMM-based system.
Proceedings Article
LSTM can Solve Hard Long Time Lag Problems
TL;DR: This work shows that problems used to promote various previous algorithms can be solved more quickly by random weight guessing than by the proposed algorithms, and uses LSTM, its own recent algorithm, to solve a hard problem.
Journal ArticleDOI
LSTM recurrent networks learn simple context-free and context-sensitive languages
Felix A. Gers,E. Schmidhuber +1 more
TL;DR: Long short-term memory (LSTM) variants are also the first RNNs to learn a simple context-sensitive language, namely a(n)b( n)c(n).