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Sensors 2018, 18(1), 209; https://doi.org/10.3390/s18010209

Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity

Department of Computer Science, Systems and Communications, University of Milano-Bicocca, Milan 20126, Italy
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Received: 21 December 2017 / Revised: 9 January 2018 / Accepted: 10 January 2018 / Published: 12 January 2018
(This article belongs to the Section Intelligent Sensors)
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Abstract

Automatic detection and localization of anomalies in nanofibrous materials help to reduce the cost of the production process and the time of the post-production visual inspection process. Amongst all the monitoring methods, those exploiting Scanning Electron Microscope (SEM) imaging are the most effective. In this paper, we propose a region-based method for the detection and localization of anomalies in SEM images, based on Convolutional Neural Networks (CNNs) and self-similarity. The method evaluates the degree of abnormality of each subregion of an image under consideration by computing a CNN-based visual similarity with respect to a dictionary of anomaly-free subregions belonging to a training set. The proposed method outperforms the state of the art. View Full-Text
Keywords: anomaly detection; defect detection; industrial quality inspection; quality control; convolutional neural networks, nanofibrous materials anomaly detection; defect detection; industrial quality inspection; quality control; convolutional neural networks, nanofibrous materials
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Napoletano, P.; Piccoli, F.; Schettini, R. Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity. Sensors 2018, 18, 209.

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