Void detection and fiber extraction for statistical characterization of fiber-reinforced polymers
Void detection and fiber extraction for statistical characterization of fiber-reinforced polymers
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DOI：
10.2352/issn.2470-1173.2020.14.coimg-250
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发表时间：
2020-01
期刊：
影响因子：
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通讯作者：
Camilo Aguilar;I. Hanhan;Ronald F. Agyei;M. Sangid;M. Comer
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文献类型：
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作者：
Camilo Aguilar;I. Hanhan;Ronald F. Agyei;M. Sangid;M. Comer
在本文中，我们提出了一种替代方法，通过结合从模型方法获得的结果来提取聚合物基质复合材料的纤维和空隙，以训练卷积神经网络。这种方法的重点是显微镜图像，其中标记的数据不容易获得，但是由于其计算复杂性，纯粹基于模型的方法可能太慢了。此外，我们提出了用于纤维实例分割范式的编码器替代方案，显示了训练和推理时间的加速，而与替代方法相对于替代方法的准确性显着降低。神经网络方法代表了超过基于模型的方法的重要速度，并且可以在大量体积中正确捕获大多数纤维和空隙，以进一步对数据进行统计分析。
In this paper we propose a surrogate approach to extract fibers and voids from polymer matrix composites by combining results obtained from model-based methods to train convolutional neural networks. This approach focuses on microscopy images where labeled data is not readily available, but purely model based approaches can be too slow due to their computational complexity. In addition, we propose an encoder-decoder alternative to a fiber instance segmentation paradigm, showing a speedup in training and inference times without a significant decrease in accuracy with respect to alternative methods. The neural networks approach represent a significant speedup over model based approaches and can correctly capture most fibers and voids in large volumes for further statistical analysis of the data.