Training Convolutional Neural Networks with Synthesized Data for Object Recognition in Industrial Manufacturing
Paper in proceeding, 2019

Visual tasks such as automated quality control or packaging require machines to be able to detect and identify objects automatically. In recent years object detection systems using deep learning have made significant advancements achieving better scores at a higher performance. However, these methods typically require large amounts of annotated images for training, which are costly and labor intensive to create. Therefore, it is an attractive alternative to generate the training data synthetically using computer-generated imagery (CGI). In this paper, we investigate how to add realistic texture to CAD objects to generate synthetic data for training of an instance segmentation network (Mask R-CNN) for recognition of manufacturing components. The results show that it is possible to create synthetic data with negligible human effort when using simple procedural materials.

Author

Jason Li

Chalmers, Electrical Engineering, Systems and control

Per-Lage Goetvall

Volvo Group

Julien Provost

Technical University of Munich

Knut Åkesson

Chalmers, Electrical Engineering, Systems and control

IEEE International Conference on Emerging Technologies and Factory Automation, ETFA

19460740 (ISSN) 19460759 (eISSN)

Vol. 2019-September 1544-1547
978-1-7281-2723-1 (ISBN)

24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)
Zaragoza, Spain,

Subject Categories

Other Computer and Information Science

Computer Science

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1109/ETFA.2019.8869484

More information

Latest update

7/17/2024