  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.
