Drone safe to launch system using machine learning
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2023
Författare
Sahlberg, Elina
Krook Willén, Björn
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Svenska Sjöräddningssällskapet has partnered with Infotiv to develop a prototype
drone and launcher system for search-and-rescue missions at sea. In this thesis
project, we investigate the possibility of automating parts of the launch sequence of
a seaborne surveillance drone. The main goal is to train a neural network model to
use a camera feed to determine if it is safe or not to launch the drone in a given
direction, and then integrate this solution with the graphic user interface through
an application programming interface. Monocular depth estimation using transfer
learning and the KITTI data set is evaluated. The KITTI data set does not contain
maritime scenery, leading to an unsatisfactory monocular depth estimation model.
U-net and convolutional neural network models are trained on the MaSTr1325 data
set, which contains semantically segmented maritime imagery. We collect additional
data for the semantic segmentation models and create a post-processing step that
evaluates if it is safe to launch or not. These models yielded satisfactory results,
and the convolutional neural network will be used by the drone operator as an extra
safety measure during launch.
Beskrivning
Ämne/nyckelord
Deep learning , monocular depth estimation , semantic segmentation , maritime environments , quantization , neural network , drone