Convolutional Networks for Voting-based Anomaly Classification in Metal Surface Inspection
6 p.
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/83012 http://hdl.handle.net/10220/42367 |
| _version_ | 1826117882194427904 |
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| author | Natarajan, Vidhya Hung, Tzu-Yi Vaikundam, Sriram Chia, Liang-Tien |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Natarajan, Vidhya Hung, Tzu-Yi Vaikundam, Sriram Chia, Liang-Tien |
| author_sort | Natarajan, Vidhya |
| collection | NTU |
| description | 6 p. |
| first_indexed | 2024-10-01T04:34:37Z |
| format | Conference Paper |
| id | ntu-10356/83012 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:34:37Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | ntu-10356/830122020-03-07T11:48:45Z Convolutional Networks for Voting-based Anomaly Classification in Metal Surface Inspection Natarajan, Vidhya Hung, Tzu-Yi Vaikundam, Sriram Chia, Liang-Tien School of Computer Science and Engineering 2017 IEEE International Conference on Industrial Technology (ICIT) Rolls-Royce@NTU Corporate Lab Anomaly classification Convolutional neural network 6 p. Automated Visual Inspection (AVI) systems for metal surface inspection is increasingly used in industries to aid human visual inspectors for classification of possible anomalies. For classification, the challenge lies in having a small and specific dataset that may easily result in over-fitting. As a solution, we propose to use deep Convolutional Neural Networks (ConvNets) learnt from the large ImageNet dataset [9] for image representations via transfer learning. Since a small dataset cannot be used to fine-tune a ConvNet due to overfitting, we also propose a Majority Voting Mechanism (MVM), which fuses the features extracted from the last three layers of ConvNets using Support Vector Machine (SVM) classifiers. This classification framework is effective where no prior knowledge of the best performing ConvNet layers is needed. This also allows flexibility in the choice of ConvNet used for feature extraction. The proposed method not only outperforms state-of-the-art traditional hand-crafted features in terms of classification but also obtains good results compared to other deep ConvNet features extracted from a pre-selected best layer on several anomaly and texture datasets. NRF (Natl Research Foundation, S’pore) Accepted Version 2017-05-11T03:43:31Z 2019-12-06T15:10:12Z 2017-05-11T03:43:31Z 2019-12-06T15:10:12Z 2017-03-01 2017 Conference Paper Natarajan, V., Hung, T.-Y., Vaikundam, S., & Chia, L.-T. (2017). Convolutional Networks for Voting-based Anomaly Classification in Metal Surface Inspection. 2017 IEEE International Conference on Industrial Technology (ICIT), 986-991. https://hdl.handle.net/10356/83012 http://hdl.handle.net/10220/42367 10.1109/ICIT.2017.7915495 199824 en © 2017 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/ICIT.2017.7915495]. application/pdf |
| spellingShingle | Anomaly classification Convolutional neural network Natarajan, Vidhya Hung, Tzu-Yi Vaikundam, Sriram Chia, Liang-Tien Convolutional Networks for Voting-based Anomaly Classification in Metal Surface Inspection |
| title | Convolutional Networks for Voting-based Anomaly Classification in Metal Surface Inspection |
| title_full | Convolutional Networks for Voting-based Anomaly Classification in Metal Surface Inspection |
| title_fullStr | Convolutional Networks for Voting-based Anomaly Classification in Metal Surface Inspection |
| title_full_unstemmed | Convolutional Networks for Voting-based Anomaly Classification in Metal Surface Inspection |
| title_short | Convolutional Networks for Voting-based Anomaly Classification in Metal Surface Inspection |
| title_sort | convolutional networks for voting based anomaly classification in metal surface inspection |
| topic | Anomaly classification Convolutional neural network |
| url | https://hdl.handle.net/10356/83012 http://hdl.handle.net/10220/42367 |
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