期刊
2021 18TH INTERNATIONAL CONFERENCE ON UBIQUITOUS ROBOTS (UR)
卷 -, 期 -, 页码 499-505出版社
IEEE
DOI: 10.1109/UR52253.2021.9494704
关键词
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类别
资金
- Technology Innovation Program [20005096]
- Ministry of Trade, Industry & Energy (MOTIE, Korea)
This paper proposes a method for food segmentation using synthetic data to address the challenges of data collection and annotations. Experimental results show that a model trained only on synthetic data can effectively segment food instances in real-world datasets, with performance improvement through fine-tuning.
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.
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