Annotated image dataset of household objects from the RoboFEI@Home team

Citation Author(s):
Douglas De Rizzo
Meneghetti
Centro Universitário FEI
Pedro Henrique Silva
Domingues
Centro Universitário FEI
Bruno de Freitas Vece
Perez
Centro Universitário FEI
Thiago Spilborghs Bueno
Meyer
Centro Universitário FEI
Kimberlin Kariny Gonçalves
Cardoso
Centro Universitário FEI
Amanda Maciel
de Lima
Centro Universitário FEI
Marina Yukari
Gonbata
Centro Universitário FEI
Fagner de Assis Moura
Pimentel
Centro Universitário FEI
Plinio Thomaz
Aquino Junior
Centro Universitário FEI
Submitted by:
Douglas De Rizz...
Last updated:
Sun, 10/04/2020 - 12:00
DOI:
10.21227/7wxn-n828
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Abstract 

Annotated image dataset of household objects from the RoboFEI@Home team

This data set contains two sets of pictures of household objects, created by the RoboFEI@Home team to develop object detection systems for a domestic robot.

The first data set was created with objects from a local supermarket. Product brands are typical from Brazil. The second data set is composed of objects from the RoboCup@Home 2018 OPL competition.

The data set contains the basic resources for the creation of custom object detection systems. Users can use the provided annotated images to train their own models and validate them in a set of test images, or they can analyze the inference time of the proposed methods in the provided videos.

Example videos of detection systems trained with this data set can be found in the following links:

Instructions: 

This data set contains two separate sets of annotated images. Common features of the image sets:

  • Images are saved in JPG format
  • Annotations are made with labelImg
  • Both sets contain videos in MP4 format to test trained detection models

Set 1

166 annotated images with 1028 objects of the following 13 classes:

  1. cereal
  2. chocolate_milk
  3. heineken
  4. iron_man
  5. medicine
  6. milk_bottle
  7. milk_box
  8. monster
  9. purple_juice
  10. red_juice
  11. shampoo
  12. tea_box
  13. yellow_juice

There are also 28 videos for testing, shot with multiple smartphones.

Set 2

388 annotated images with 1737 objects of the following 20 classes:

  1. apple
  2. basket
  3. cereal
  4. chocolate_drink
  5. cloth_opl
  6. coke
  7. crackers
  8. grape_juice
  9. help_me_carry_opl
  10. noodles
  11. orange
  12. orange_juice
  13. paprika
  14. potato_chips
  15. pringles
  16. sausages
  17. scrubby
  18. sponge_opl
  19. sprite
  20. tray

There is also a single long video and 398 unannotated images for testing.