Towards DeepSpray: Using Convolutional Neural Network to post-process Shadowgraphy Images of Liquid Atomization

TL;DR

This paper explores the use of convolutional neural networks to improve analysis of liquid atomization images, demonstrating promising results in droplet detection, shape prediction, and feature annotation, paving the way for a unified DeepSpray framework.

Contribution

It introduces a CNN-based approach for post-processing liquid atomization images, addressing multiple tasks with minimal data and demonstrating high potential for integrated analysis.

Findings

CNN models effectively detect and segment droplets in degraded images.

Successful prediction of ellipse geometries from limited visual data.

Manual annotation of liquid features shows CNN applicability across experimental and simulated images.

Abstract

This technical report investigates the potential of Convolutional Neural Networks to post-process images from primary atomization. Three tasks are investigated. First, the detection and segmentation of liquid droplets in degraded optical conditions. Second, the detection of overlapping ellipses and the prediction of their geometrical characteristics. This task corresponds to extrapolate the hidden contour of an ellipse with reduced visual information. Third, several features of the liquid surface during primary breakup (ligaments, bags, rims) are manually annotated on 15 experimental images. The detector is trained on this minimal database using simple data augmentation and then applied to other images from numerical simulation and from other experiment. In these three tasks, models from the literature based on Convolutional Neural Networks showed very promising results, thus demonstrating the high potential of Deep Learning to post-process liquid atomization. The next step is to embed these models into a unified framework DeepSpray.