Automatic detection and classification of manufacturing defects in metal boxes using deep neural networks.
This paper develops a new machine vision framework for efficient detection and classification of manufacturing defects in metal boxes. Previous techniques, which are based on either visual inspection or on hand-crafted features, are both inaccurate and time consuming. In this paper, we show that by...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2018-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC6226149?pdf=render |
| _version_ | 1811207131129446400 |
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| author | Oumayma Essid Hamid Laga Chafik Samir |
| author_facet | Oumayma Essid Hamid Laga Chafik Samir |
| author_sort | Oumayma Essid |
| collection | DOAJ |
| description | This paper develops a new machine vision framework for efficient detection and classification of manufacturing defects in metal boxes. Previous techniques, which are based on either visual inspection or on hand-crafted features, are both inaccurate and time consuming. In this paper, we show that by using autoencoder deep neural network (DNN) architecture, we are able to not only classify manufacturing defects, but also localize them with high accuracy. Compared to traditional techniques, DNNs are able to learn, in a supervised manner, the visual features that achieve the best performance. Our experiments on a database of real images demonstrate that our approach overcomes the state-of-the-art while remaining computationally competitive. |
| first_indexed | 2024-04-12T03:59:55Z |
| format | Article |
| id | doaj.art-db85833b127d4a33a2fecce2b91c3576 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-12T03:59:55Z |
| publishDate | 2018-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-db85833b127d4a33a2fecce2b91c35762022-12-22T03:48:44ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-011311e020319210.1371/journal.pone.0203192Automatic detection and classification of manufacturing defects in metal boxes using deep neural networks.Oumayma EssidHamid LagaChafik SamirThis paper develops a new machine vision framework for efficient detection and classification of manufacturing defects in metal boxes. Previous techniques, which are based on either visual inspection or on hand-crafted features, are both inaccurate and time consuming. In this paper, we show that by using autoencoder deep neural network (DNN) architecture, we are able to not only classify manufacturing defects, but also localize them with high accuracy. Compared to traditional techniques, DNNs are able to learn, in a supervised manner, the visual features that achieve the best performance. Our experiments on a database of real images demonstrate that our approach overcomes the state-of-the-art while remaining computationally competitive.http://europepmc.org/articles/PMC6226149?pdf=render |
| spellingShingle | Oumayma Essid Hamid Laga Chafik Samir Automatic detection and classification of manufacturing defects in metal boxes using deep neural networks. PLoS ONE |
| title | Automatic detection and classification of manufacturing defects in metal boxes using deep neural networks. |
| title_full | Automatic detection and classification of manufacturing defects in metal boxes using deep neural networks. |
| title_fullStr | Automatic detection and classification of manufacturing defects in metal boxes using deep neural networks. |
| title_full_unstemmed | Automatic detection and classification of manufacturing defects in metal boxes using deep neural networks. |
| title_short | Automatic detection and classification of manufacturing defects in metal boxes using deep neural networks. |
| title_sort | automatic detection and classification of manufacturing defects in metal boxes using deep neural networks |
| url | http://europepmc.org/articles/PMC6226149?pdf=render |
| work_keys_str_mv | AT oumaymaessid automaticdetectionandclassificationofmanufacturingdefectsinmetalboxesusingdeepneuralnetworks AT hamidlaga automaticdetectionandclassificationofmanufacturingdefectsinmetalboxesusingdeepneuralnetworks AT chafiksamir automaticdetectionandclassificationofmanufacturingdefectsinmetalboxesusingdeepneuralnetworks |