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DroGate : Lightweight Real-Time Drone Perception System, for Alphapilot competition

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DroGate : Lightweight Real-Time Drone Perception System

Authors: Zeryab Moussaoui, Yacine Ben Ameur , Houssem Meghnouj

11 Mars 2019: Submit the code according to Alpha-Pilot deadline.

10 May 2019: Add technical report

The aim of this code is to complete the selection of the Lockheed Martin's Drone Race "Alpha Pilot" (https://www.herox.com/alphapilot/77-test-2) but can be used for another applications by using Transfer Learning.

The Alpha Pilot qualification evaluates team's skills in both Computer Vision and Machine Learning, by predicting gate locations on images :

Architecture

DroGate is built on the efficient ResNet-based architecture. The convolution stage of the architecture consists of a fast ResNet-8 with 3 residual blocks, followed by dropout and ReLU non-linearity. The extracted features are then processed by two separates multilayer perceptrons to predict gate locations and their confidence score :

Requirements

  • Python (recommanded : 3.5)
  • Tensorflow (recommanded : 1.12.0)
  • OpenCV (recommanded : 4.0.0)
  • Seaborn (recommanded : 0.9.0)

And also some librairies : numpy, python-wget, zipfile, json.

Training

To (re) train your own Drogate, just follow instructions of Drogate_Training notebook.

Technical details

Please read following article : DroGate, a Lightweight Real-Time Drone Perception System

Related Publications

If you use of our code or our report in your project , please cite :

 @article{Drogate,
  title={{Drogate}: a Lightweight Real-Time Drone Perception System},
  author={Zeryab Moussaoui and al.},
  journal={Github},
  year={2019}
 }

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