SafeUAV: Learning to estimate depth and safe landing areas for UAVs from synthetic data
Team members
Team members
Alina Marcu, Dragos Costea, Vlad Licaret and Mihai Pirvu
Supervisors
Supervisors
Prof. dr. Marius Leordeanu
Prof. dr. Emil Slusanschi
Idea
Idea
Predict safe landing areas for UAVs. Define a segmentation problem, where 'horizontal' mean safe.
Pipeline
Pipeline
- extract RGB, depth and safe landing information from Google Earth
- train our networks on synthetic data
- test on real drone footage
Example data:
Our CNN outperform state-of-the-art methods on semantic segmentation:
- Safe landing area task:
- Depth estimation task:
Paper
Paper
eccv2018camerareadykit_uavision2018.pdf
Links
Links
Code
Pytorch code for reproducing all experiments presented in the paper:
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
Slides
Presentation slides, pptx or pdf format:
Cite
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},
}