# Deep Learning-Based Tracking and Lineage Reconstruction of Ligament Breakup

DevFeed: [Deep Learning-Based Tracking and Lineage Reconstruction of Ligament Breakup](<https://devfeed.tech/articles/deep-learning-based-tracking-and-lineage-reconstruction-of-ligament-breakup-123115.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2604.08711>)

Author: Vrushank Ahire, Vivek Kurumanghat, Mudasir Ganaie, Lipika Kabiraj

Published: 2026-11-09T05:00:00Z

Content type: article

Language: en

Sources: [arXiv: Artificial Intelligence](<https://devfeed.tech/sources/arxiv-artificial-intelligence.md>), [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [Image processing](<https://devfeed.tech/topics/image-processing.md>), [data augmentation](<https://devfeed.tech/topics/data-augmentation.md>), [Physics-guided deep learning](<https://devfeed.tech/topics/physics-guided-deep-learning.md>)

Tags: [analysis](<https://devfeed.tech/tags/analysis.md>), [cs-ai](<https://devfeed.tech/tags/cs-ai.md>), [cs-cv](<https://devfeed.tech/tags/cs-cv.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [droplets](<https://devfeed.tech/tags/droplets.md>), [fragmentation](<https://devfeed.tech/tags/fragmentation.md>), [framework](<https://devfeed.tech/tags/framework.md>), [one-to-many](<https://devfeed.tech/tags/one-to-many.md>), [physics](<https://devfeed.tech/tags/physics.md>), [robustness](<https://devfeed.tech/tags/robustness.md>), [size](<https://devfeed.tech/tags/size.md>), [statistics](<https://devfeed.tech/tags/statistics.md>), [synthetic-data-generation](<https://devfeed.tech/tags/synthetic-data-generation.md>), [tracking](<https://devfeed.tech/tags/tracking.md>), [transformer](<https://devfeed.tech/tags/transformer.md>)

## AI overview

The two-stage framework detects droplets and ligaments in high-speed images, then tracks their relationships while allowing one ligament to fragment into multiple objects. Its Faster R-CNN detector, trained with morphology-preserving synthetic data, reached a held-out F1 score of up to 0.872 across fourteen original-to-synthetic data configurations; robustness was tested across atomization conditions and liquid compositions. A Transformer-augmented multilayer perceptron classifies continuation, fragmentation, or non-association using physics-informed geometric features. On the held-out test set, it achieved 86.1% accuracy and 93.2% precision, with perfect recall for fragmentation despite severe class imbalance. The approach preserves parent-child lineage to support breakup statistics such as fragment multiplicity and droplet size distributions. Results described are for recordings of two impinging Carbopol gel jets.

## Source excerpt

arXiv:2604.08711v2 Announce Type: replace-cross Abstract: The disintegration of liquid sheets into ligaments and droplets involves highly transient, multi-scale dynamics that are complex to quantify from high-speed shadowgraphy images. Identification of droplets, ligaments, and blobs formed by the breakup of a liquid sheet, and their tracking from frame to frame, is an essential part of spray analysis. Analysis of such processes poses a fundamental challenge for conventional multi-object tracking frameworks, which enforce strict one-to-one temporal associations and cannot represent one-to-many fragmentation events. We present a two-stage deep learning framework that detects droplets and ligaments and identifies their temporal relationships across successive frames, modeling ligament deformation, fragmentation, and parent-child lineage. In the first stage, a Faster R-CNN detector with a ResNet-50 backbone and Feature Pyramid Network classifies ligaments and droplets in recordings of two impinging Carbopol gel jets. A morphology-preserving synthetic data generation strategy enriches the training set without physically implausible configurations, achieving a held-out test F1 score of up to 0.872 across fourteen original-to-synthetic data configurations, with robustness tested on different atomization conditions and liquid compositions. In the second stage, a Transformer-augmented multilayer perceptron classifies inter-frame object associations into continuation, fragmentation (one-to-many), and non-association using physics-informed geometric features. Despite severe class imbalance, the model achieves 86.1% accuracy, 93.2% precision, and perfect recall (1.00) for fragmentation events on the held-out test set. Together, the two stages preserve parent-child lineage and enable extraction of breakup statistics, including fragment multiplicity and droplet size distributions, offering an automated analysis of the primary mode of atomization.