Estimating Regions of Deterioration in Electron Microscope Images of Rubber Materials via a Transfer Learning-Based Anomaly Detection Model

• Togo, Ren

• Saito, Naoki

• Ogawa, Takahiro

• Haseyama, Miki

抄録

A method for estimating regions of deterioration in electron microscope images of rubber materials is presented in this paper. Deterioration of rubber materials is caused by molecular cleavage, external force, and heat. An understanding of these characteristics is essential in the eld of material science for the development of durable rubber materials. Rubber material deterioration can be observed by using on electron microscope but it requires much effort and specialized knowledge to nd regions of deterioration. In this paper, we propose an automated deterioration region estimation method based on deep learning and anomaly detection techniques to support such material development. Our anomaly detection model, called Transfer Learning-based Deep Autoencoding Gaussian Mixture Model (TL-DAGMM), uses only normal regions for training since obtaining training data for regions of deterioration is dif cult. TL-DAGMMmakes use of extracted high representation features from a pre-trained deep learning model and can automatically learn the characteristics of normal rubber material regions. Regions of deterioration are estimated at the pixel level by calculated anomaly scores. Experiments on real rubber material electron microscope images demonstrated the effectiveness of our model.

収録刊行物

• IEEE Access

  IEEE Access 7 162395-162404, 2019-11

  IEEE

キーワード

• Materials informatics
• anomaly detection
• deep learning
• transfer learning

詳細情報

• CRID: 1050845763996948480
• NII論文ID: 120006767554
• DOI: 10.1109/access.2019.2950972
• ISSN: 21693536
• HANDLE: 2115/76163
• Web Site: http://xplorestaging.ieee.org/ielx7/6287639/8600701/08889738.pdf?arnumber=8889738
• 本文言語コード: en
• 資料種別: journal article
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