Searching for rare traffic signs
Typ
Examensarbete för masterexamen
Program
Computer systems and networks (MPCSN), MSc
Publicerad
2021
Författare
Gustafsson, Hannes
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Deep neural networks are good at recognizing traffic signs when they are trained
on many different examples of each one. However, some traffic signs are very rare
and not often encountered when collecting data. This means that a network does
not recognize rare traffic signs as well as those that are encountered often. When
collecting large amounts of data, one usually only labels a small subset of it. Therefore,
there might exist more examples of the rare traffic signs in the unlabeled data
set. If these examples could be found and used in training, the performance of the
model could be expected to improve. This thesis evaluates how a standard neural
network performs in searching for rare traffic signs, and whether some commonly
used techniques from few-shot learning can improve its performance. To our surprise
we find that they cannot. Furthermore, in this thesis we show that searching for rare
traffic signs is an efficient active learning method, outperforming other established
methods by requiring up to 8x less additional data to achieve the same F1-score on
rare traffic sign recognition.
Beskrivning
Ämne/nyckelord
Computer , science , computer science , engineering , project , thesis , deep learning