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A Generic Semi-supervised Deep Learning-Based Approach for Automated Surface Inspection
2020
IEEE Access
Preserved Fulltext
In this paper, a generic semi-supervised deep learning-based approach for ASI that requires a small quantity of labeled training data is proposed. ...
INDEX TERMS Automated surface inspection, defect detection, deep learning, machine vision, MixMatch, semi-supervised learning. ...
CONCLUSION In this paper, a generic semi-supervised deep learning approach that requires a small quantity of labeled data for automated surface defect inspection is proposed. ...
doi:10.1109/access.2020.3003588
fatcat:wm3gcgqaq5dgzpaeebtiiysmwi
Attention-guided Quality Assessment for Automated Cryo-EM Grid Screening
[article]
2020
arXiv
pre-print
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XCryoNet is a semi-supervised, attention-guided deep learning approach that provides explainable scoring of automatically extracted square images using limited amounts of labeled data. ...
Here, we focus on automating the early decision making for the microscope operator, scoring low magnification images of squares, and proposing the first deep learning framework, XCryoNet, for automated ...
Conclusion We have presented XCryoNet, a semi-supervised, attention-guided deep learning approach that provides interpretable scoring of automatically extracted cryo-EM grid squares using limited amounts ...
arXiv:2007.05593v2
fatcat:geq34cu4pzginpyjfgdakjb4yy
Semi-Supervised Surface Anomaly Detection of Composite Wind Turbine Blades From Drone Imagery
[article]
2021
arXiv
pre-print
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These clusters are then processed by a suite of semi-supervised detection methods. ...
BladeNet also obtains an AUC of 0.639 for surface anomaly detection across the Ørsted blade inspection dataset. ...
Deep Autoencoding Models for Unsupervised
based approach for detecting surface-fault anoma- Anomaly Segmentation in Brain MR Images. ...
arXiv:2112.00556v1
fatcat:iizs5l7klfgb5cvbk6fdpsbr64
Automated quality assurance as an intelligent cloud service using machine learning
2019
Procedia CIRP
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This publication therefore presents a service-based system for optical quality assurance using machine learning algorithms. ...
This publication therefore presents a service-based system for optical quality assurance using machine learning algorithms. ...
Acknowledgements The OpenServ4P research and development project (www.openserv4p.de) is funded by the German Federal Ministry for Economic Affairs and Energy (BMWi) within the "Smart Service World" framework ...
doi:10.1016/j.procir.2020.01.034
fatcat:k425yr3tejcnfn6qmccgm4tevu
Semi-supervised Anomaly Detection using AutoEncoders
[article]
2020
arXiv
pre-print
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But for defect detection lack of availability of a large number of anomalous instances and labelled data is a problem. ...
However, manual inspection is slow, tedious, subjective and susceptible to human biases. Therefore, the automation of defect detection is desirable. ...
Acknowledgments We thank the Ontario Ministry of Transportation and NSERC (National Science and Research Council) for providing funds that supported this research. ...
arXiv:2001.03674v1
fatcat:uq4tpz33fjck3aecrmn3k5wtdy
Smart-Inspect: Micro Scale Localization and Classification of Smartphone Glass Defects for Industrial Automation
[article]
2020
arXiv
pre-print
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We present a robust semi-supervised learning framework for intelligent micro-scaled localization and classification of defects on a 16K pixel image of smartphone glass. ...
In addition, we incorporated two classifiers at different stages of our inspection framework for labeling and refining the unlabeled defects. ...
Semi-supervised learning methods exist such as Pseudo-Label [16] , learning using deep generative models [17] and with ladder networks [18] , and learning by association [19] ; however, these approaches ...
arXiv:2010.00741v1
fatcat:5c4z5ki7nrclzlpl3ecsngtbra
Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY
2020
Sensors
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Finally, we report the state of the art for approaching the detection and classification of defects through supervised and non-supervised classifiers and deep learning. ...
This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles. ...
SpA, Robot System Automation srl, Roggi srl and Robotech srl.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s20051459
pmid:32155900
fatcat:rsdnszztffbadllniclol3pjvi
SELF-TRAINING FOR SEMI-SUPERVISED DEEP CONTOUR DETECTION OF SURFACE WATER
2022
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Given the cost of data labeling, we propose a deep semi-supervised self-learning system performed in two training stages, known as teacher-student. ...
For that purpose, we present a semi-automated method for collecting and labeling water contours from Landsat-8 and Sentinel-2 images. ...
Two methods were used for Sentinel-2 data collection, one for supervised learning and one for semi-supervised learning. ...
doi:10.5194/isprs-archives-xliii-b3-2022-1393-2022
fatcat:oamjry4c7fhldgaez5lfqof3hi
A weakly supervised surface defect detection based on Convolutional Neural Network
2020
IEEE Access
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The deep learning-based methods recently developed for defect detection are typically trained using a supervised learning strategy and large defect sample sets. ...
Surface defect detection is a critical task in product quality assurance for manufacturing lines. ...
The authors are grateful to all of the reviewers for suggestions and insights that improved the paper. ...
doi:10.1109/access.2020.2977821
fatcat:eenu2l26gbcx7olmgiqdridfou
Artificial Intelligence Assisted Infrastructure Assessment Using Mixed Reality Systems
[article]
2018
arXiv
pre-print
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This study explains in detail the described system and related methodologies of implementing attention guided semi supervised deep learning into mixed reality technology, which interacts with the human ...
This study aims to create a smart, human centered method that offers significant contributions to infrastructure inspection, maintenance, management practice, and safety for the bridge owners. ...
Kevin Pfeil, doctoral student, from the department of Computer Science at UCF for invaluable discussions and feedback. ...
arXiv:1812.05659v1
fatcat:3x2ni3kvqrhzdctefzw3ypnxyy
Semi Supervised Deep Quick Instance Detection and Segmentation
[article]
2021
arXiv
pre-print
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The overall approach is based on the tutor-child analogy in which a deep network (tutor) is pretrained for class-agnostic object detection which generates labeled data for another deep network (child). ...
In this paper, we present a semi supervised deep quick learning framework for instance detection and pixel-wise semantic segmentation of images in a dense clutter of items. ...
For this reason, we call it semi-supervised labeling as the mask or box is generated by the tutor while a meaningful label is provided by human.
B. ...
arXiv:2101.06405v1
fatcat:tqd3wnp7zrft5j6vriepkfgj7m
Digital reality: a model-based approach to supervised learning from synthetic data
2019
AI Perspectives
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In this position paper, we present the Digital Reality concept are a structured approach to generate training data synthetically. ...
The central idea is to simulate measurements based on scenes that are generated by parametric models of the real world. ...
White box adaptive sampling Semi-automated approaches for generating training data can also rely on inspecting the used neural network. ...
doi:10.1186/s42467-019-0002-0
fatcat:p4ttyucarfabfjhpvp56gvex2i
Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials
2020
Sensors
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This paper attempts to present a comprehensive survey on both two-dimensional and three-dimensional surface defect detection technologies based on reviewing over 160 publications for some typical metal ...
The computer-vision-based surface defect detection of metal planar materials is a research hotspot in the field of metallurgical industry. ...
The generative adversarial network (GAN) [124] , consisting of two deep neural networks (i.e., a generator and discriminator), is a typical semi-supervised learning method. ...
doi:10.3390/s20185136
pmid:32916943
fatcat:qele6iuawnayrkjwywhkhjrmae
Intelligent Railway Foreign Object Detection: A Semi-supervised Convolutional Autoencoder Based Method
[article]
2021
arXiv
pre-print
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It consists of three different modules, a bottleneck feature generator as encoder, a photographic image generator as decoder, and a reconstruction discriminator developed via adversarial learning. ...
In this paper, we develop a semi-supervised convolutional autoencoder based framework that only requires railway track images without prior knowledge on the foreign objects in the training process. ...
We propose a new RFOD method, a semi-supervised CAE-based method, for facilitating the railway inspection automation. ...
arXiv:2108.02421v1
fatcat:argr7th2s5e3dnuyqdj4a2ycv4
CNN-Based Defect Inspection for Injection Molding Using Edge Computing and Industrial IoT Systems
2021
Applied Sciences
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In this study, we proposed a defect inspection system for injection molding in edge intelligence. ...
Currently, the development of automated quality inspection is drawing attention as a major component of the smart factory. ...
[41] applied a general deep learning approach based on CNN models for automatic surface examination. Star et al. ...
doi:10.3390/app11146378
fatcat:zgtxgmzx2vae7dx6zph4etedoe
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