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Machine-learning based Hybrid Method for Surface Defect Detection and Categorization in PU Foam

by Shailendra Singh Kathait, Sakshi Mathur
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 25
Year of Publication: 2018
Authors: Shailendra Singh Kathait, Sakshi Mathur
10.5120/ijca2018917668

Shailendra Singh Kathait, Sakshi Mathur . Machine-learning based Hybrid Method for Surface Defect Detection and Categorization in PU Foam. International Journal of Computer Applications. 181, 25 ( Nov 2018), 1-6. DOI=10.5120/ijca2018917668

@article{ 10.5120/ijca2018917668,
author = { Shailendra Singh Kathait, Sakshi Mathur },
title = { Machine-learning based Hybrid Method for Surface Defect Detection and Categorization in PU Foam },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2018 },
volume = { 181 },
number = { 25 },
month = { Nov },
year = { 2018 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number25/30089-2018917668/ },
doi = { 10.5120/ijca2018917668 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:07:01.558251+05:30
%A Shailendra Singh Kathait
%A Sakshi Mathur
%T Machine-learning based Hybrid Method for Surface Defect Detection and Categorization in PU Foam
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 25
%P 1-6
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Foam making is an important industry, their main applications being foam mattresses. Hence, their production in the industries is subject to very strict safety checks to ensure their quality. There are many types of defects that can arise during their manufacturing process, like holes, cuts, a misconfiguration in the material and many more. These defects are reviewed manually which leads to an inadequate accuracy and many defects are not detected. This paper proposes a novel approach that identifies defects in the foam material and on the surface using a hybrid method. Both supervised and unsupervised approaches are used to categorize materials based on normal or defective, including the type of defect. Then the reliable model is chosen according to the precision rates of both the models.

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Index Terms

Computer Science
Information Sciences

Keywords

Polymerization Data Augmentation Computer vision OpenCV Image Processing Machine learning Deep Learning Convolutional Neural Networks

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