{
  "id": "2107.07191",
  "version": "v1",
  "published": "2021-07-15T08:36:54.000Z",
  "updated": "2021-07-15T08:36:54.000Z",
  "title": "Deep Learning based Food Instance Segmentation using Synthetic Data",
  "authors": [
    "D. Park",
    "J. Lee",
    "J. Lee",
    "K. Lee"
  ],
  "comment": "Technical Report",
  "categories": [
    "cs.CV",
    "cs.AI"
  ],
  "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. Our code and pre-trained weights are avaliable online at: https://github.com/gist-ailab/Food-Instance-Segmentation",
  "revisions": [
    {
      "version": "v1",
      "updated": "2021-07-15T08:36:54.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "synthetic data",
      "food instance segmentation",
      "software blender placing multiple",
      "blender placing multiple objects",
      "data collection"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}