Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/83012
Title: Convolutional Networks for Voting-based Anomaly Classification in Metal Surface Inspection
Authors: Natarajan, Vidhya
Hung, Tzu-Yi
Vaikundam, Sriram
Chia, Liang-Tien
Keywords: Anomaly classification
Convolutional neural network
Issue Date: 2017
Source: 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.
Abstract: 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.
Description: 6 p.
URI: https://hdl.handle.net/10356/83012
http://hdl.handle.net/10220/42367
DOI: 10.1109/ICIT.2017.7915495
Rights: © 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].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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