Automated Feature Tracking for Real-Time Kinematic Analysis and Shape Estimation of Carbon Nanotube Growth

2025-08-26

AI Summary

Current in situ characterization of carbon nanotube (CNT) dynamic growth faces two key bottlenecks: ex situ methods yield only static snapshots, while existing in situ techniques rely on manual initialization and fail to resolve continuous, single-particle trajectories. To address this, we propose VFTrack—a fully automated framework for detecting, tracking, and multimodally decomposing CNT growth dynamics (axial elongation, lateral drift, and oscillation) directly from scanning electron microscopy (SEM) image sequences—without human intervention. VFTrack integrates the ALIKED feature detector with the LightGlue matcher, augmented by particle tracking and motion vector decoupling algorithms. Evaluated on 13,540 annotated trajectories, it achieves an F1-score of 0.78 and an α-score of 0.89. The framework successfully reconstructs spatially heterogeneous growth rates and time-resolved morphological evolution, establishing a new paradigm for mechanistic studies of nanoscale in situ growth processes.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Topic discovery and tracking
• Security and Privacy: Tracking, profiling, and countermeasures against them

Abstract

Carbon nanotubes (CNTs) are critical building blocks in nanotechnology, yet the characterization of their dynamic growth is limited by the experimental challenges in nanoscale motion measurement using scanning electron microscopy (SEM) imaging. Existing ex situ methods offer only static analysis, while in situ techniques often require manual initialization and lack continuous per-particle trajectory decomposition. We present Visual Feature Tracking (VFTrack) an in-situ real-time particle tracking framework that automatically detects and tracks individual CNT particles in SEM image sequences. VFTrack integrates handcrafted or deep feature detectors and matchers within a particle tracking framework to enable kinematic analysis of CNT micropillar growth. A systematic using 13,540 manually annotated trajectories identifies the ALIKED detector with LightGlue matcher as an optimal combination (F1-score of 0.78, $α$-score of 0.89). VFTrack motion vectors decomposed into axial growth, lateral drift, and oscillations, facilitate the calculation of heterogeneous regional growth rates and the reconstruction of evolving CNT pillar morphologies. This work enables advancement in automated nano-material characterization, bridging the gap between physics-based models and experimental observation to enable real-time optimization of CNT synthesis.

Problem

• Automated tracking of carbon nanotube growth dynamics
• Real-time kinematic analysis from SEM image sequences
• Continuous per-particle trajectory decomposition for morphology reconstruction

Innovation

• Automated real-time particle tracking framework
• Integrates feature detectors and matchers
• Decomposes motion vectors for morphology reconstruction