MATLAB object-detection Computer vision dataset Deep learning
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cs-chan

Description

Exclusively Dark (ExDARK) dataset which to the best of our knowledge, is the largest collection of low-light images taken in very low-light environments to twilight (i.e 10 different conditions) to-date with image class and object level annotations.

244 Stars 54 Forks BSD 3-Clause "New" or "Revised" License 98 Commits 3 Opened issues

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Exclusively Dark (ExDark) Image Dataset (Official Site)

ArXiv

Updated on June 02, 2019 (Code for low-light image enhancement is released)

Updated on Oct. 31, 2018 (Accepted for publication in CVIU)

Released on May 29, 2018

Description

In order to facilitate a new object detection and image enhancement research particularly in the low-light environment, we introduce the Exclusively Dark (ExDark) dataset (CVIU2019). The Exclusively Dark (ExDARK) dataset is a collection of 7,363 low-light images from very low-light environments to twilight (i.e 10 different conditions) with 12 object classes (similar to PASCAL VOC) annotated on both image class level and local object bounding boxes.

demo

Source Code

The source code for our work on low-light image enhancement is now available. Please refer to the SPIC folder.

Citation

If you find this dataset useful for your research, please cite

@article{Exdark,
  title={Getting to Know Low-light Images with The Exclusively Dark Dataset},
  author={Loh, Yuen Peng and Chan, Chee Seng},
  journal={Computer Vision and Image Understanding},
  volume={178},
  pages={30-42},
  year={2019},
  doi={https://doi.org/10.1016/j.cviu.2018.10.010}
}

Feedback

Suggestions and opinions on this dataset (both positive and negative) are greatly welcomed. Please contact the authors by sending an email to

lexloh2009 at hotmail.com
or
cs.chan at um.edu.my
.

License and Copyright

The project is open source under BSD-3 license (see the

LICENSE
file).

For commercial purpose usage, please contact Dr. Chee Seng Chan at

cs.chan at um.edu.my

©2018-2019 Center of Image and Signal Processing, Faculty of Computer Science and Information Technology, University of Malaya.

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