Semantic Segmentation in Marine Environment: Using 2D spherical projection and convolutional neural networks
Typ
Examensarbete för masterexamen
Program
Communication Engineering (MPCOM), MSc
Publicerad
2022
Författare
Dahlin, Emma
Jonsson, Hanna
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
The marine environment is constantly changing and therefore it can be difficult
to control a vessel under these conditions. In this thesis, a method is proposed
to interpret the surroundings to aid easy and safe travels on water. This is done
through semantic segmentation by transforming 3D point clouds to 2D images, using
a projection-based method. The transformation enables training with convolutional
neural networks to achieve a fast and high performance network.
The above method is successfully implemented in the marine environment and the
results show that fewer classes are preferable to reach a high accuracy of the model.
The features from the environment was unbalanced, which was compensated for by
implementing a loss function that weighted the underrepresented classes higher. The
model increased in performance for the minority classes. Furthermore, the real-time
semantic segmentation was slower compared to the sensors update-time but there
are possibilities to reduce the prediction time in future work. Precipitation was hard
to detect due to low amounts of annotated data but the other surroundings could
be detected in harsh weather conditions either way. The results show promising
outcome for future implementation.
Beskrivning
Ämne/nyckelord
Machine learning , Semantic segmentation , CNN , Marine environment , LiDAR , U-Net