Block, L.L.BlockRaiser, AdrianAdrianRaiserSchön, L.L.SchönBraun, F.F.BraunRiedel, OliverOliverRiedel2022-11-232022-11-232022https://publica.fraunhofer.de/handle/publica/42908510.1016/j.procir.2022.05.0042-s2.0-85132258761Training datasets for image recognition are poorly available for small and medium-sized manufacturing companies, due to the specialized products they work with, and the disproportionate investment to generate their own ones. Thus, we investigate a new approach: The Image-Bot consists of a physical apparatus and a processing pipeline to generate training datasets from real-world objects easily. It takes pictures of the objects in front of a green screen and blends them with random backgrounds. The approach was tested with 23 objects and a YOLOv5 algorithm. It creates a state-of-the-art training dataset with about 2,000 images per object in under 45 min.enChroma KeyingImage RecognitionLow-EffortObject DetectionSMESynthetic Training Datajournal article