AbstractObject detection methods based on deep learning have made great progress in recent years and have been used successfully in many different applications. However, since they have been evaluated predominantly on datasets of natural images, it is still unclear how accurate and effective they can be if used in special domain applications, for example in scientific, industrial, etc. images, where the properties of the images are very different from those taken in natural scenes. In this study, we illustrate the challenges one needs to face in such a setting on a concrete practical application, involving the detection of a particular fluid phenomenon—bag-breakup—in images of droplet scattering, which differ significantly from natural images. Using two technologically mature and state-of-the-art object detection methods, RetinaNet and YOLOv7, we discuss what strategies need to be considered in this problem setting, and perform both quantitative and qualitative evaluations to study their effects. Additionally, we also propose a new method to further improve accuracy of detection by utilizing information from several consecutive frames. We hope that the practical insights gained in this study can be of use to other researchers and practitioners when targeting applications where the images differ greatly from natural images.