Name
Abstract | ||
|---|---|---|
In the process of intelligently segmenting foods in images using deep neural networks for diet management, data collection and labeling for network training are very important but labor-intensive tasks. In order to solve the difficulties of data collection and annotations, this paper proposes a food segmentation method applicable to real-world through synthetic data. To perform food segmentation on healthcare robot systems, such as meal assistance robot arm, we generate synthetic data using the open-source 3D graphics software Blender placing multiple objects on meal plate and train Mask R-CNN for instance segmentation. Also, we build a data collection system and verify our segmentation model on real-world food data. As a result, on our real-world dataset, the model trained only synthetic data is available to segment food instances that are not trained with 52.2% mask AP@all, and improve performance by +6.4%p after fine-tuning comparing to the model trained from scratch. In addition, we also confirm the possibility and performance improvement on the public dataset for fair analysis. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/UR52253.2021.9494704 | 2021 18th International Conference on Ubiquitous Robots (UR) |
Keywords | DocType | ISSN |
deep neural networks,synthetic data,open-source 3D graphics software Blender,meal plate,data collection system,food instance segmentation,healthcare robot systems,train Mask R-CNN | Conference | 2325-033X |
ISBN | Citations | PageRank |
978-1-6654-4601-3 | 0 | 0.34 |
References | Authors | |
0 | 4 | |