Unsupervised surface defect detection using deep autoencoders and data augmentation
Surface level defect detection, such as detecting missing components, misalignments and physical damages, is an important step in any manufacturing process. In this paper, similarity matching techniques for manufacturing defect detection are discussed. We are proposing an algorithm which detects sur...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2020
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| Online Access: | https://hdl.handle.net/10356/137972 |
| _version_ | 1811679234316304384 |
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| author | Abdul Mujeeb Dai, Wenting Erdt, Marius Sourin, Alexei |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Abdul Mujeeb Dai, Wenting Erdt, Marius Sourin, Alexei |
| author_sort | Abdul Mujeeb |
| collection | NTU |
| description | Surface level defect detection, such as detecting missing components, misalignments and physical damages, is an important step in any manufacturing process. In this paper, similarity matching techniques for manufacturing defect detection are discussed. We are proposing an algorithm which detects surface level defects without relying on the availability of defect samples for training. Furthermore, we are also proposing a method which works when only one or a few reference images are available. It implements a deep autoencoder network and trains input reference image(s) along with various copies automatically generated by data augmentation. The trained network is then able to generate a descriptor-a unique signature of the reference image. After training, a test image of the same product is sent to the trained network to generate a test image descriptor. By matching the reference and test descriptors, a similarity score is generated which indicates if a defect is found. Our experiments show that this approach is more generic than traditional hand-engineered feature extraction methods and it can be applied to detect multiple type of defects. |
| first_indexed | 2024-10-01T03:05:55Z |
| format | Conference Paper |
| id | ntu-10356/137972 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:05:55Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1379722020-04-20T13:38:13Z Unsupervised surface defect detection using deep autoencoders and data augmentation Abdul Mujeeb Dai, Wenting Erdt, Marius Sourin, Alexei School of Computer Science and Engineering School of Electrical and Electronic Engineering 2018 International Conference on Cyberworlds (CW) Fraunhofer Research Center Engineering::Computer science and engineering Defect Detection Automatic Optical Inspection Surface level defect detection, such as detecting missing components, misalignments and physical damages, is an important step in any manufacturing process. In this paper, similarity matching techniques for manufacturing defect detection are discussed. We are proposing an algorithm which detects surface level defects without relying on the availability of defect samples for training. Furthermore, we are also proposing a method which works when only one or a few reference images are available. It implements a deep autoencoder network and trains input reference image(s) along with various copies automatically generated by data augmentation. The trained network is then able to generate a descriptor-a unique signature of the reference image. After training, a test image of the same product is sent to the trained network to generate a test image descriptor. By matching the reference and test descriptors, a similarity score is generated which indicates if a defect is found. Our experiments show that this approach is more generic than traditional hand-engineered feature extraction methods and it can be applied to detect multiple type of defects. NRF (Natl Research Foundation, S’pore) Accepted version 2020-04-20T13:22:05Z 2020-04-20T13:22:05Z 2018 Conference Paper Abdul Mujeeb, Dai, W., Erdt, M., & Sourin, A. (2018). Unsupervised surface defect detection using deep autoencoders and data augmentation. Proceedings of the 2018 International Conference on Cyberworlds (CW), 391-398. doi:10.1109/cw.2018.00076 https://hdl.handle.net/10356/137972 10.1109/cw.2018.00076 391 398 en SMA-RP4 © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/CW.2018.00076 application/pdf |
| spellingShingle | Engineering::Computer science and engineering Defect Detection Automatic Optical Inspection Abdul Mujeeb Dai, Wenting Erdt, Marius Sourin, Alexei Unsupervised surface defect detection using deep autoencoders and data augmentation |
| title | Unsupervised surface defect detection using deep autoencoders and data augmentation |
| title_full | Unsupervised surface defect detection using deep autoencoders and data augmentation |
| title_fullStr | Unsupervised surface defect detection using deep autoencoders and data augmentation |
| title_full_unstemmed | Unsupervised surface defect detection using deep autoencoders and data augmentation |
| title_short | Unsupervised surface defect detection using deep autoencoders and data augmentation |
| title_sort | unsupervised surface defect detection using deep autoencoders and data augmentation |
| topic | Engineering::Computer science and engineering Defect Detection Automatic Optical Inspection |
| url | https://hdl.handle.net/10356/137972 |
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