Journal article
Automatic detection and classification of manufacturing defects in metal boxes using deep neural networks
PLoS ONE, Vol.13(11), e0203192
2018
Abstract
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.
Details
- Title
- Automatic detection and classification of manufacturing defects in metal boxes using deep neural networks
- Authors/Creators
- O. Essid (Author/Creator)H. Laga (Author/Creator)C. Samir (Author/Creator)
- Publication Details
- PLoS ONE, Vol.13(11), e0203192
- Publisher
- Public Library of Science
- Identifiers
- 991005540719307891
- Copyright
- © 2018 Essid et al.
- Murdoch Affiliation
- School of Engineering and Information Technology
- Language
- English
- Resource Type
- Journal article
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Source: InCites
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- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.17 Computer Vision & Graphics
- 4.17.128 Deep Visual Recognition
- Web Of Science research areas
- Computer Science, Artificial Intelligence
- ESI research areas
- Engineering