Active learning in deep convolutional neural networks for image segmentation
Typ
Examensarbete för masterexamen
Program
Publicerad
2022
Författare
Schwartz, Isak
Åkvist, William
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
The sitting position and seat belt orientation of passengers in automobiles can be
crucial in the event of a collision. In order to warn passengers of unsafe positions,
deep learning models in the form of neural networks can be used to identify the
seat belt from image data. Performance of neural networks can be increased by
improving the model (model-centric approaches) or by improving the data used to
train the model (data-centric approaches).
In this thesis we compare the segmentation performance gains from model-centric
approaches to data-centric approaches including stratified sampling, data balancing,
label error reduction and active learning. No new model architecture was found that
improved performance, but the model training time was sped up by four times without
performance loss. Stratified sampling, data balancing and label error reduction
did not improve performance significantly.
In active learning, images to be labeled were selected according to the model’s uncertainty.
Several uncertainty metrics were used, all leading to an improvement when
using active learning. The best result showed that we achieved 95% and 99% of the
best baseline performance using 19% and 23% less data respectively.
Beskrivning
Ämne/nyckelord
computer vision , image segmentation , active learning , data-centric AI