Estimating Regions of Deterioration in Electron Microscope Images of Rubber Materials via a Transfer Learning-Based Anomaly Detection Model
Estimating Regions of Deterioration in Electron Microscope Images of Rubber Materials via a Transfer Learning-Based Anomaly Detection Model
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DOI：
10.1109/access.2019.2950972
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发表时间：
2019-11
期刊：
影响因子：
3.9
通讯作者：
Ren Togo;Naoki Saito;Takahiro Ogawa;M. Haseyama
中科院分区：
文献类型：
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作者：
Ren Togo;Naoki Saito;Takahiro Ogawa;M. Haseyama
本文提出了一种估算橡胶材料电子显微图像中劣化区域的方法。橡胶材料的劣化是由分子分裂、外力和热引起的。了解这些特性对于材料科学领域开发耐用橡胶材料至关重要。橡胶材料的劣化可以通过使用电子显微镜观察到，但需要花费大量精力和专业知识来找到劣化区域。在本文中，我们提出了一种基于深度学习和异常检测技术的自动退化区域估计方法，以支持此类材料的开发。我们的异常检测模型称为基于迁移学习的深度自动编码高斯混合模型（TL-DAGMM），它只使用正常区域进行训练，因为很难获得恶化区域的训练数据。TL-DAGMM利用从预训练的深度学习模型中提取的高代表性特征，可以自动学习正常橡胶材料区域的特征。通过计算的异常分数在像素级估计劣化区域。对真实的橡胶材料的电子显微镜图像的实验证明了该模型的有效性。
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 field 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 find 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 difficult. TL-DAGMM makes 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.