On Loom Fabric Defect Inspection Using Contact Image Sensors And Activation Layer Embedded Convolutional Neural Network
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On Loom Fabric Defect Inspection Using Contact Image Sensors And Activation Layer Embedded Convolutional Neural Network
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Author : Wenbin Ouyang
language : en
Publisher:
Release Date : 2018
On Loom Fabric Defect Inspection Using Contact Image Sensors And Activation Layer Embedded Convolutional Neural Network written by Wenbin Ouyang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Image converters categories.
Malfunctions on loom machines are the main causes of faulty fabric production. An on-loom fabric inspection system is a real-time monitoring device that enables immediate defect detection for human intervention. This dissertation presented a solution for the on-loom fabric defect inspection, including the new hardware design-the configurable contact image sensor (CIS) module-for on-loom fabric scanning and the defect detection algorithms. The main contributions of this work include (1) creating a configurable CIS module adaptable to a loom width, which brings CIS unique features, such as sub-millimeter resolution, compact size, short working distance and low cost, to the fabric defect inspection system, (2) designing a two-level hardware architecture that can be efficiently deployed in a weaving factory with hundreds of looms, (3) developing a two-level inspecting scheme, with which the initial defect screening is performed on the Raspberry Pi and the intensive defect verification is processed on the cloud server, (4) introducing the novel pairwise-potential activation layer to a convolutional neural network that leads to high accuracies of defect segmentation on fabrics with fine and imbalanced structures, (5) achieving a real-time defect detection that allows a possible defect to be examined multiple times, and (6) implementing a new color segmentation technique suitable for processing multi-color fabric defects. The novel CIS-based on-loom scanning system offered real-time and high-resolution fabric images, which was able to deliver the information of single thread on a fabric. The algorithm evaluation on the fabric defect datasets showed a non-miss-detection rate on defect-free fabrics. The average precision of defect existed images reached above 90% at the pixel level. The detected defect pixels' integrity-the recall scored around 70%. Possible defect regions overestimated on ground truth images and the morphologies of fine defects similar to regular fabric pattern were the two major reasons that caused the imperfection in defect pixel locating. The experiments showed the defect areas on multi-color fabrics could be precisely located under the proposed color segmentation algorithm.
Automatic Printed Fabric Defect Detection Using A Convolutional Neural Network
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Author : Samit Chakraborty
language : en
Publisher:
Release Date : 2021
Automatic Printed Fabric Defect Detection Using A Convolutional Neural Network written by Samit Chakraborty and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Fabric Defect Detection By Wav
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Author : Tin-Chi Lee
language : en
Publisher: Open Dissertation Press
Release Date : 2017-01-27
Fabric Defect Detection By Wav written by Tin-Chi Lee and has been published by Open Dissertation Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-27 with Technology & Engineering categories.
This dissertation, "Fabric Defect Detection by Wavelet Transform and Neural Network" by Tin-chi, Lee, 李天賜, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled Submitted by LEE Tin Chi for the degree of Master of Philosophy at The University of Hong Kong in July 2004 Textile inspection plays an important role in maintaining the quality of products. In this thesis, three methods which utilize matched masks, wavelet transform and neural network are proposed for fabric defect detection. An evaluation of the performance of the methods was conducted on eight classes of fabric defects (Broken End, Dirty Yarn, Mispick, Netting Multiples, Slack End, Thick Bar, Thin Bar, and Wrong Draw). In the first method, a multi-channel filtering bank equipped with five matched masks was used. Matched masks are 2-D filters that characterize specific texture properties. They are designed to emphasize the Wrong Draw texture, the Mispick texture, the horizontal edges, the bars structure and the filled regions on fabric images. At the filter outputs, segmentation by thresholds is applied, followed by a logical OR operation. The total number of pixels exceeding the threshold on the resulting image determines whether the fabric image is defective or defect-free. Using this method, 96% of fabric defects were successfully detected, and the false alarm rate was 6%. The method achieved a 90% - 100% detection rate for most fabric defects, though the detection rate for Thin Bar defects was only 75%. The second method employed wavelet transform to decompose fabric images into multi-scales and orientations. During the training stage, the parameters to be optimized include the rotation angles and the two thresholds applied on the horizontal and vertical transformed images. The variation in rotation angles determines the selection of wavelet bases. During the detection stage, the discrimination criterion is based on the total number of defect windows. Using this method, only 76% of fabric defects were identified, and the false alarm rate was 7%. The detection rate for Dirty Yarn was high, but much lower for Broken End and Wrong Draw defects. The last method took advantage of the fault tolerance and learning ability of neural networks. We explored the texture structure of defect-free images so that feature extraction was conducted on repeating units with proper selection of locations. For defect images, similar feature vectors were extracted and passed to the neural network. Using this method, the detection rate was as high as 92% and the false alarm rate was 6%. Dirty Yarn, Netting Multiples, Mispick, Thin Bar and Wrong Draw defects were completely identified, while 75% of Broken End and Slack End defects were detected. However, only 73% of Thin Bar defects were detected. The method employing matched masks proved the most effective in detecting fabric defects. The neural network method was next best. The wavelet transform method was the least effective, because it was only able to detect effectively certain classes of fabric defects. Dirty Yarn, Netting Multiples, Mispick and Slack End defects are relatively easy to identify correctly. Wrong Draw and Thin Bar defects are less easy to detect and Broken End and Thick Bar defects are the most difficult to detect. DOI: 10.5353/th_b2928728 Subjects: Wavelets (Mathematics) Neural networks (Computer science) Textile fabrics - Testing
On Loom Real Time Noncontact Detection Of Fabric Defects By Ultrasonic Imaging
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Author :
language : en
Publisher:
Release Date : 1998
On Loom Real Time Noncontact Detection Of Fabric Defects By Ultrasonic Imaging written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with categories.
A noncontact, on-loom ultrasonic inspection technique was developed for real-time 100% defect inspection of fabrics. A prototype was built and tested successfully on loom. The system is compact, rugged, low cost, requires minimal maintenance, is not sensitive to fabric color and vibration, and can easily be adapted to current loom configurations. Moreover, it can detect defects in both the pick and warp directions. The system is capable of determining the size, location, and orientation of each defect. To further improve the system, air-coupled transducers with higher efficiency and sensitivity need to be developed. Advanced detection algorithms also need to be developed for better classification and categorization of defects in real-time.
On Loom Fabric Defect Detection
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Author : Dorian Schneider
language : en
Publisher:
Release Date : 2015
On Loom Fabric Defect Detection written by Dorian Schneider and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Deep Learning With Azure
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Author : Mathew Salvaris
language : en
Publisher: Apress
Release Date : 2018-08-24
Deep Learning With Azure written by Mathew Salvaris and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-24 with Computers categories.
Get up-to-speed with Microsoft's AI Platform. Learn to innovate and accelerate with open and powerful tools and services that bring artificial intelligence to every data scientist and developer. Artificial Intelligence (AI) is the new normal. Innovations in deep learning algorithms and hardware are happening at a rapid pace. It is no longer a question of should I build AI into my business, but more about where do I begin and how do I get started with AI? Written by expert data scientists at Microsoft, Deep Learning with the Microsoft AI Platform helps you with the how-to of doing deep learning on Azure and leveraging deep learning to create innovative and intelligent solutions. Benefit from guidance on where to begin your AI adventure, and learn how the cloud provides you with all the tools, infrastructure, and services you need to do AI. What You'll Learn Become familiar with the tools, infrastructure, and services available for deep learning on Microsoft Azure such as Azure Machine Learning services and Batch AI Use pre-built AI capabilities (Computer Vision, OCR, gender, emotion, landmark detection, and more) Understand the common deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs) with sample code and understand how the field is evolving Discover the options for training and operationalizing deep learning models on Azure Who This Book Is For Professional data scientists who are interested in learning more about deep learning and how to use the Microsoft AI platform. Some experience with Python is helpful.
Nano Chips 2030
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Author : Boris Murmann
language : en
Publisher: Springer Nature
Release Date : 2020-06-08
Nano Chips 2030 written by Boris Murmann and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-08 with Science categories.
In this book, a global team of experts from academia, research institutes and industry presents their vision on how new nano-chip architectures will enable the performance and energy efficiency needed for AI-driven advancements in autonomous mobility, healthcare, and man-machine cooperation. Recent reviews of the status quo, as presented in CHIPS 2020 (Springer), have prompted the need for an urgent reassessment of opportunities in nanoelectronic information technology. As such, this book explores the foundations of a new era in nanoelectronics that will drive progress in intelligent chip systems for energy-efficient information technology, on-chip deep learning for data analytics, and quantum computing. Given its scope, this book provides a timely compendium that hopes to inspire and shape the future of nanoelectronics in the decades to come.
Computer Technology For Textiles And Apparel
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Author : Jinlian Hu
language : en
Publisher: Elsevier
Release Date : 2011-07-14
Computer Technology For Textiles And Apparel written by Jinlian Hu and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-07-14 with Computers categories.
Computer technology has transformed textiles from their design through to their manufacture and has contributed to significant advances in the textile industry. Computer technology for textiles and apparel provides an overview of these innovative developments for a wide range of applications, covering topics including structure and defect analysis, modelling and simulation, and apparel design. The book is divided into three parts. Part one provides a review of different computer-based technologies suitable for textile materials, and includes chapters on computer technology for yarn and fabric structure analysis, defect analysis and measurement. Chapters in part two discuss modelling and simulation principles of fibres, yarns, textiles and garments, while part three concludes with a review of computer-based technologies specific to apparel and apparel design, with themes ranging from 3D body scanning to the teaching of computer-aided design to fashion students. With its distinguished editor and international team of expert contributors, Computer technology for textiles and apparel is an invaluable tool for a wide range of people involved in the textile industry, from designers and manufacturers to fibre scientists and quality inspectors. Provides an overview of innovative developments in computer technology for a wide range of applications Covers structure and defect analysis, modelling and simulation and apparel design Themes range from 3D body scanning to the teaching of computer-aided design to fashion students
Composite Materials
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Author : Kamal K. Kar
language : en
Publisher: Springer
Release Date : 2016-10-14
Composite Materials written by Kamal K. Kar and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-14 with Technology & Engineering categories.
Composite materials are used as substitutions of metals/traditional materials in aerospace, automotive, civil, mechanical and other industries. The present book collects the current knowledge and recent developments in the characterization and application of composite materials. To this purpose the volume describes the outstanding properties of this class of advanced material which recommend it for various industrial applications.
Wipo Technology Trends 2019 Artificial Intelligence
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Author : World Intellectual Property Organization
language : en
Publisher: WIPO
Release Date : 2019-01-21
Wipo Technology Trends 2019 Artificial Intelligence written by World Intellectual Property Organization and has been published by WIPO this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-21 with Law categories.
The first report in a new flagship series, WIPO Technology Trends, aims to shed light on the trends in innovation in artificial intelligence since the field first developed in the 1950s.