[2604.08711] Deep Learning-Based Tracking and Lineage Reconstruction of Ligament Breakup

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.08711 (cs) [Submitted on 9 Apr 2026] Title:Deep Learning-Based Tracking and Lineage Reconstruction of Ligament Breakup Authors:Vrushank Ahire, Vivek Kurumanghat, Mudasir Ganaie, Lipika Kabiraj View a PDF of the paper titled Deep Learning-Based Tracking and Lineage Reconstruction of Ligament Breakup, by Vrushank Ahire and 3 other authors View PDF HTML (experimental) 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 im...