Towards Fully-Synthetic Training for Industrial Applications

This paper proposes a scalable approach for synthetic image generation of industrial objects leveraging Blender for image rendering. In addition to common components in synthetic image generation research, three novel features are presented: First, we model relations between target objects and randomly apply those during scene generation (Object Relation Modelling (ORM)). Second, we extend the idea of distractors and create Object-alike Distractors (OAD), resembling the textural appearance (i.e. material and size) of target objects. And third, we propose a Mixed-lighting Illumination (MLI), combining global and local light sources to automatically create a diverse illumination of the scene. In addition to the image generation approach we create an industry-centered dataset for evaluation purposes. Experiments show, that our approach enables fully synthetic training of object detectors for industrial use-cases. Moreover, an ablation study provides evidence on the performance boost in object detection when using our novel features.     «

This paper proposes a scalable approach for synthetic image generation of industrial objects leveraging Blender for image rendering. In addition to common components in synthetic image generation research, three novel features are presented: First, we model relations between target objects and randomly apply those during scene generation (Object Relation Modelling (ORM)). Second, we extend the idea of distractors and create Object-alike Distractors (OAD), resembling the textural appearance (i.e....     »