Published November 8, 2024 | Version v1
Dataset Open

FlyingArUco v2 Dataset

Description

This is a synthetic dataset composed of images containing various ArUco markers overlaid on backgrounds sampled from the MS COCO 2017 training dataset, used to train the models in DeepArUco++: improved detection of square fiducial markers in challenging lighting conditions. The contents of each file are the following:

  • flyingarucov2.tar.gz: Base FlyingArUco v2 images, without any augmentation applied. For each image a .json file is provided with the ground-truth positions of each marker and encoded IDs.
  • det_luma.tar.gz: Detection dataset built from the base FlyingArUco v2 dataset, using only (simulated) changes in lighting in order to obtain augmented samples.
  • det_luma_b.tar.gz: Detection dataset built from the base FlyingArUco v2 dataset, using (simulated) changes in lighting and blur in order to obtain augmented samples.
  • det_luma_bc.tar.gz: Detection dataset built from the base FlyingArUco v2 dataset, using (simulated) changes in lighting, blur and color shift in order to obtain augmented samples.
  • det_luma_bn.tar.gz: Detection dataset built from the base FlyingArUco v2 dataset, using (simulated) changes in lighting, blur and gaussian noise in order to obtain augmented samples.
  • det_luma_bnc.tar.gz: Detection dataset built from the base FlyingArUco v2 dataset, using (simulated) changes in lighting, blur, gaussian noise and color shift in order to obtain augmented samples.
  • reg_luma_bc.tar.gz: Corner refinement (regression)/marker decoding dataset built from the det_luma_bc detection dataset.

Files

Files (12.2 GB)

Name Size Download all
md5:72d283c7c8446a3befafb704c67577cc
2.3 GB Download
md5:e74878608440153c26146f359af18247
2.2 GB Download
md5:c911dd42bd8bd89ef08809fcc6b16399
2.1 GB Download
md5:5c8c24e4664ea0c35465b03f351347ca
2.5 GB Download
md5:327e8d03e40d2d6cfb9b2a2f8e703249
2.5 GB Download
md5:d8438f39385bfc989028d9c7d70ca0be
277.7 MB Download
md5:bc414a3bf416327c55cfe6181b66f7fd
222.8 MB Download

Additional details

Related works

Is described by
Journal article: 10.1016/j.imavis.2024.105313 (DOI)
Is part of
Journal article: 10.1016/j.imavis.2024.105313 (DOI)