TY - GEN
T1 - Training Convolutional Neural Networks with Synthesized Data for Object Recognition in Industrial Manufacturing
AU - Li, Jason
AU - Götvall, Per Lage
AU - Provost, Julien
AU - Åkesson, Knut
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85074197225&partnerID=8YFLogxK
U2 - 10.1109/ETFA.2019.8869484
DO - 10.1109/ETFA.2019.8869484
M3 - Conference contribution
AN - SCOPUS:85074197225
T3 - IEEE International Conference on Emerging Technologies and Factory Automation, ETFA
SP - 1544
EP - 1547
BT - Proceedings - 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2019
Y2 - 10 September 2019 through 13 September 2019
ER -