(43. GIL-Jahrestagung, Resiliente Agri-Food-Systeme, 2023) Wübben, Henning; Butz, Raphaela; von Szadkowski, Kai; Barenkamp, Marco
Image augmentation is a key component in computer vision pipelines. Its techniques utilize different levels of data annotation. A lack of methods can be observed when it comes to data that supplies depth maps, in particular synthetic data. We propose a novel augmentation method named DepthAug that utilizes depth annotations in image data and examine its performance in the context of object detection tasks. Results show a boost in MAP score performance compared to previous related methods.