Deep Learning-Based Tracking and Lineage Reconstruction of Ligament Breakup
2026-04-09 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionArtificial IntelligenceMachine Learning
AI summaryⓘ
The authors developed a two-step deep learning method to better understand how liquid sheets break apart into smaller pieces like ligaments and droplets. First, their model identifies these pieces in high-speed videos of a gel jet, using synthetic images to improve accuracy. Then, they track how pieces split or move over time, even capturing cases where one piece breaks into many. Their approach helps automatically map out the breakup process and gather important details about the droplets formed. This makes studying spray breakup easier and more detailed than before.
liquid sheet breakupligamentsdropletsFaster R-CNNResNet-50Feature Pyramid NetworkTransformermulti-object trackingfragmentationatomization
Authors
Vrushank Ahire, Vivek Kurumanghat, Mudasir Ganaie, Lipika Kabiraj
Abstract
The disintegration of liquid sheets into ligaments and droplets involves highly transient, multi-scale dynamics that are difficult to quantify from high-speed shadowgraphy images. Identifying droplets, ligaments, and blobs formed during breakup, along with tracking across frames, is essential for spray analysis. However, conventional multi-object tracking frameworks impose strict one-to-one temporal associations and cannot represent one-to-many fragmentation events. In this study, we present a two-stage deep learning framework for object detection and temporal relationship modeling across frames. The framework captures ligament deformation, fragmentation, and parent-child lineage during liquid sheet disintegration. In the first stage, a Faster R-CNN with a ResNet-50 backbone and Feature Pyramid Network detects and classifies ligaments and droplets in high-speed shadowgraphy recordings of an impinging Carbopol gel jet. A morphology-preserving synthetic data generation strategy augments the training set without introducing physically implausible configurations, achieving a held-out F1 score of up to 0.872 across fourteen original-to-synthetic configurations. In the second stage, a Transformer-augmented multilayer perceptron classifies inter-frame 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. Together, the framework enables automated reconstruction of fragmentation trees, preservation of parent-child lineage, and extraction of breakup statistics such as fragment multiplicity and droplet size distributions. By explicitly identifying children droplets formed from ligament fragmentation, the framework provides automated analysis of the primary atomization mode.