Environmental facilities.

Nanoparticles exhibit broad applications in materials mechanics, medicine, energy and other fields. The ordered arrangement of nanoparticles is very important to fully understand their properties and functionalities. However, in materials science, the acquisition of training images requires a large number of professionals and the labor cost is extremely high, so there are usually very few training samples in the field of materials. In this study, a segmentation method of nanoparticle topological structure based on synthetic data (SD) is proposed, which aims to solve the issue of small data in the field of materials. Our findings reveal that the combination of SD generated by rendering software with merely 15% Authentic Data (AD) shows better performance in training deep learning model. The trained U-Net model shows that Miou of 0.8476, accuracy of 0.9970, Kappa of 0.8207, and Dice of 0.9103, respectively. Compared with data enhancement alone, our approach yields a 1% improvement in the Miou metric. These results show that our proposed strategy can achieve better prediction performance without increasing the cost of data acquisition.

纳米颗粒在材料力学、医药、能源等诸多领域拥有广泛应用。纳米颗粒的有序排布对于充分解析其物性与功能至关重要。然而在材料科学领域，训练图像的获取需要大量专业人员投入，人力成本极高，因此该领域的训练样本通常极为稀缺。本研究提出了一种基于合成数据（Synthetic Data，SD）的纳米颗粒拓扑结构分割方法，旨在解决材料领域的数据小样本问题。研究结果表明，将渲染软件生成的合成数据与仅占比15%的真实数据（Authentic Data，AD）相结合，在深度学习模型训练中表现更优。经训练的U-Net模型各项指标分别为：Miou为0.8476、准确率为0.9970、Kappa系数为0.8207以及Dice系数为0.9103。与仅使用数据增强的方案相比，本方法的Miou指标提升了1%。上述结果表明，所提策略无需增加数据采集成本，即可获得更优异的预测性能。