Autonomous flying drone for vehicle classification
| Title | Autonomous flying drone for vehicle classification |
|---|---|
| Summary | Building an autonomous flying drone for vehicle classification |
| Keywords | Deep learning, robotics, drone, navigation, obstacle avoidance |
| TimeFrame | VT2022 |
| References | |
| Prerequisites | Deep learning, image analysis |
| Author | |
| Supervisor | Cristofer Englund, Fernando Alonso-Fernandez, Martin Torstensson |
| Level | Master |
| Status | Open |
Building an autonomous flying drone for vehicle classification
Problem Vehicle detection and identification is one of the basic building blocks for many applications within traffic safety. Ground-based autonomous vehicles or land drones have difficulties in performing this task due mainly to the location of the sensors they carry. Land drones carrying cameras located at such low heights are not able to capture the necessary information for performing accurate classification algorithms. Solution The proposed solution takes cameras to a higher point of view (compared to a wheel-based ground vehicle) where images can collect a more complete and clearer picture of all objects in the environment. In the case of vehicle detection and classification, a flying drone could give more freedom to find good images of the vehicles in the environment. Moreover, a flying drone allows for faster movements which translates into shorter times of operation. It will be necessary to study the different options that exists in the market for ready-to-fly drones and other hardware and software. One of the options available in the market that could be considered for this project is the CrazyFly 2.1 drone (https://store.bitcraze.io/products/crazyflie-2-1) together with the Drone Autonomy Suite from CentEye (http://www.centeye.com/drones-and-robotics/perception-and-autonomy-suite/) On the software side; first, it will be necessary to collect as many images as possible from different vehicle manufacturers and their different car models in order to properly train a ML algorithm. Results should be presented including an accurate description of the validation methodology.
