On Loom Fabric Defect Detection PDF Download
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Author: Dorian Schneider
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author: Dorian Schneider
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author: Wenbin Ouyang
Publisher:
ISBN:
Category : Image converters
Languages : en
Pages : 106
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Book Description
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.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 11
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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.
Author: Savarimuthu Sabeenian
Publisher: LAP Lambert Academic Publishing
ISBN: 9783659455292
Category :
Languages : en
Pages : 128
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Book Description
Sona Signal and Image PROcessing Research Centre (Sona SIPRO) [A Unit of Sona College of Technology] was formally inaugurated by the Former Scientist President of India Dr.A.P.J.Abdul Kalam in 2009. Since then a team of Engineers have been working to solve many social problems using Information Science. This work on Fabric Defect Detection in Hand loom Cottage Silk Industries is a work funded by the All India Council for Technical Education under the Research Promotion Scheme. This book is a outcome of the research work carried out to cater the requirements of small scale hand loom silk weavers located in Salem (TN), India.
Author: Yu-Jin Zhang
Publisher: Springer
ISBN: 3319219693
Category : Computers
Languages : en
Pages : 615
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Book Description
This book constitutes the refereed conference proceedings of the 8th International Conference on Image and Graphics, ICIG 2015 held in Tianjin, China, in August 2015. The 164 revised full papers and 6 special issue papers were carefully reviewed and selected from 339 submissions. The papers focus on various advances of theory, techniques and algorithms in the fields of images and graphics.
Author: Tin-Chi Lee
Publisher: Open Dissertation Press
ISBN: 9781374716926
Category :
Languages : en
Pages :
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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
Author: Samit Chakraborty
Publisher:
ISBN:
Category :
Languages : en
Pages : 139
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Book Description
Author: Rajkishore Nayak
Publisher: Woodhead Publishing
ISBN: 0081011334
Category : Technology & Engineering
Languages : en
Pages : 428
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Book Description
Automation in Garment Manufacturing provides systematic and comprehensive insights into this multifaceted process. Chapters cover the role of automation in design and product development, including color matching, fabric inspection, 3D body scanning, computer-aided design and prototyping. Part Two covers automation in garment production, from handling, spreading and cutting, through to finishing and pressing techniques. Final chapters discuss advanced tools for assessing productivity in manufacturing, logistics and supply-chain management. This book is a key resource for all those engaged in textile and apparel development and production, and is also ideal for academics engaged in research on textile science and technology. Delivers theoretical and practical guidance on automated processes that benefit anyone developing or manufacturing textile products Offers a range of perspectives on manufacturing from an international team of authors Provides systematic and comprehensive coverage of the topic, from fabric construction, through product development, to current and potential applications
Author: Calvin Wong
Publisher: Woodhead Publishing
ISBN: 0081012187
Category : Technology & Engineering
Languages : en
Pages : 314
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Book Description
Applications of Computer Vision in Fashion and Textiles provides a systematic and comprehensive discussion of three key areas that are taking advantage of developments in computer vision technology, namely textile defect detection and quality control, fashion recognition and 3D modeling, and 2D and 3D human body modeling for improving clothing fit. It introduces the fundamentals of computer vision techniques for fashion and textile applications, also reviewing computer vision techniques for textile quality control, including chapters on wavelet transforms, Gibor filters, Fourier transforms, and neural network techniques. Final sections cover recognition, modeling, retrieval technologies and advanced human shape modeling techniques. The book is essential reading for scientists and researchers working in the field of fashion production, quality assurance, product development, textiles, fashion supply chain managers, R&D professionals and managers in the textile industry. Explores computer vision technology with reference to improving budget, quality and schedule control in textile manufacturing Provides a thorough understanding of the role of computer vision in developing intelligent systems for the fashion and textiles industries Elucidates the connections between human body modeling technology and intelligent manufacturing systems
Author: Dash, Sujata
Publisher: IGI Global
ISBN: 152252858X
Category : Computers
Languages : en
Pages : 567
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Book Description
The digital age is ripe with emerging advances and applications in technological innovations. Mimicking the structure of complex systems in nature can provide new ideas on how to organize mechanical and personal systems. The Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms is an essential scholarly resource on current algorithms that have been inspired by the natural world. Featuring coverage on diverse topics such as cellular automata, simulated annealing, genetic programming, and differential evolution, this reference publication is ideal for scientists, biological engineers, academics, students, and researchers that are interested in discovering what models from nature influence the current technology-centric world.
