DroneVehicle-C — Corrupted DroneVehicle Test Split

DroneVehicle-C is a derivative of the DroneVehicle dataset (Sun et al., 2022), generated by applying a systematic sensor-degradation corruption pipeline to the original test split. It is intended for benchmarking multimodal RGB+TIR fusion robustness in drone-based vehicle detection, and is released for academic and non-commercial use only in accordance with the CC BY-NC-SA 3.0 license of the original work.

Attribution

Original dataset: DroneVehicle — Sun, Y., Cao, B., Zhu, P., & Hu, Q. "Drone-Based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning." IEEE Transactions on Circuits and Systems for Video Technology, 2022.

• GitHub: https://github.com/VisDrone/DroneVehicle
• Paper: https://arxiv.org/abs/2003.02437

Corruptions applied by: Saksham Singh Birla, University of Twente (BSc thesis, TSCiT 2025).

Note: An unofficial HuggingFace mirror of DroneVehicle exists at McCheng/DroneVehicle but should not be treated as the authoritative source. The original dataset and its license terms are defined by the VisDrone project at Tianjin University (https://github.com/VisDrone/DroneVehicle).

License

This dataset is released under Creative Commons Attribution-NonCommercial- ShareAlike 3.0 (CC BY-NC-SA 3.0), the same license as the original DroneVehicle dataset distributed by the VisDrone project. Commercial use is prohibited.

Structure

Each .tar.gz contains one corruption condition with rgb/ and ir/ subdirectories. labels.tar.gz contains DOTA-format oriented bounding box annotations shared across all conditions.

Conditions (23 corrupted + 1 clean)

Modality	Corruption	Severities
RGB	gaussian_noise	s1, s2, s3
RGB	motion_blur	s1, s2, s3
RGB	brightness_shift	s1, s2, s3
RGB	low_contrast	s1, s2, s3
RGB	complete_dropout	—
TIR	sensor_noise	s1, s2, s3
TIR	blur	s1, s2, s3
TIR	intensity_shift	s1, s2, s3
TIR	complete_dropout	—

Citation

If you use DroneVehicle-C, please cite the original DroneVehicle dataset:

@ARTICLE{sun2022UA_CMDet,
  author  = {Sun, Yiming and Cao, Bing and Zhu, Pengfei and Hu, Qinghua},
  journal = {IEEE Transactions on Circuits and Systems for Video Technology},
  title   = {Drone-Based RGB-Infrared Cross-Modality Vehicle Detection Via
             Uncertainty-Aware Learning},
  year    = {2022},
  volume  = {32},
  number  = {10},
  pages   = {6700--6713},
  doi     = {10.1109/TCSVT.2022.3168279}
}