SafeUAV: Learning to estimate depth and safe landing areas for UAVs from synthetic data

Example data:

Our CNN outperform state-of-the-art methods on semantic segmentation:

• Safe landing area task:

• Depth estimation task:

Paper

Links

Code

Pytorch code for reproducing all experiments presented in the paper:

Gitlab repo

Dataset and snapshots

• Dataset: two suburban and two urban datasets
• Snapshots: 4 pretrained models on both depth estimation and safe landing tasks
  • SafeUAV-Net-Large and SafeUAV-Net-Small trained for each task

Dataset + snapshots onedrive

Slides

Presentation slides, pptx or pdf format:

• slides pptx
• slides pdf

Cite

Marcu, Alina and Costea, Dragos and Licaret, Vlad and Pirvu, Mihai and Leordeanu, Marius and Slusanschi, Emil. "SafeUAV: Learning to estimate depth and safe landing areas for UAVs from synthetic data." European Conference on Computer Vision (ECCV) UAVision Workshop. 2018.

@inproceedings{safeuav2018marcu,

  title={SafeUAV: Learning to estimate depth and safe landing areas for UAVs from synthetic data},

  author={Marcu, Alina and Costea, Dragos and Licaret, Vlad and Pirvu, Mihai and Leordeanu, Marius and Slusanschi, Emil},

  booktitle={European Conference on Computer Vision (ECCV) UAVision Workshop},

  year={2018},

}