This study addresses the challenge of analyzing the growth kinetics of carbon nanotubes using in-situ homodyne polarization microscopy (HPM) by developing an automated deep learning (DL) approach. A Mask-RCNN architecture, enhanced with a ResNet-50 backbone, was employed to recognize and track individual nanotubes in microscopy videos, significantly improving the efficiency and reproducibility of kinetic data extraction. The method involves a series of video processing steps to enhance contrast and used differential treatment techniques to manage low signal and fast kinetics. The DL model demonstrates consistency with manual measurements and increased throughput, laying the foundation for statistical studies of nanotube growth. The approach can be adapted for other types of in-situ microscopy studies, emphasizing the importance of automation in high-throughput data acquisition for research on individual nano-objects.

翻译：本研究针对利用原位同调偏振显微镜（HPM）分析碳纳米管生长动力学的挑战，开发了一种自动化的深度学习方法。采用以ResNet-50为骨干网络增强的Mask-RCNN架构，在显微视频中识别并跟踪单个纳米管，显著提高了动力学数据提取的效率和可重复性。该方法包含一系列增强对比度的视频处理步骤，并运用差分处理技术应对低信号与快速动力学问题。该深度学习模型与人工测量结果保持一致且提高了通量，为纳米管生长的统计研究奠定了基础。此方法可适用于其他类型的原位显微研究，突显了自动化在高通量获取单个纳米物体研究数据中的重要性。