论文标题
使用计算机视觉神经网络自动检测e词的树突
Automatic detection of equiaxed dendrites using computer vision neural networks
论文作者
论文摘要
在固化中经常遇到e骨树突。它们通常大量形成，这使得人眼几乎不可能进行检测，本地化和跟踪。在本文中，我们展示了如何利用机器学习领域的最新进展来解决此问题，并提出了计算机视觉神经网络以自动检测e词的树突。我们的网络是使用相磁模拟结果训练的，并且适当的数据增加允许在固化条件下执行与模拟训练完全不同的检测任务。例如，在这里我们展示了它们如何在微重力固化实验中成功检测到各种大小的树突。我们讨论了培训此类网络的挑战以及我们的解决方案，并将神经网络的性能与传统的形状检测方法进行比较。
Equaixed dendrites are frequently encountered in solidification. They typically form in large numbers, which makes their detection, localization, and tracking practically impossible for a human eye. In this paper, we show how recent progress in the field of machine learning can be leveraged to tackle this problem and we present computer vision neural network to automatically detect equiaxed dendrites. Our network is trained using phase-field simulation results, and proper data augmentation allows to perform the detection task in solidification conditions entirely different from those simulated for training. For example, here we show how they can successfully detect dendrites of various sizes in a microgravity solidification experiment. We discuss challenges in training such a network along with our solutions for them, and compare the performance of neural network with traditional methods of shapes detection.
