Modelling temporal context for traffic light recognition using RNNs
Typ
Examensarbete för masterexamen
Program
Publicerad
2021
Författare
Björnsson, David Freyr
Westerberg, Mattias
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Abstract
The purpose of this thesis is to investigate whether or not including temporal context
using recurrent neural networks in real-time object detection systems can improve detection
performance in traffic light recognition. This was investigated using the DriveU
traffic light dataset. Two variations of the YOLOv4 object detection system were created.
The first variation is a LSTM which takes as input the bounding boxes predicted
by YOLOv4 and outputs updated predictions. The second variation is a modification
of the YOLOv4 network in which convolutional layers are replaced with convolutional
LSTMs. With a limited number of experiments, it was found that the baseline model outperforms
the more complicated sequential models. However, there is evidence that this
is due to the sequential training strategy since the YOLOv4 baseline was outperformed
by some sequential models when it adopted the sequential training strategy. The baseline
YOLOv4 model achieved best performance on a held-out test set. The best sequential
model achieved lower detection performance. When the baseline YOLOv4 was trained
with the sequential training strategy, it achieved worse performance than the sequential
models. Modelling temporal context using recurrent neural networks may improve detection
performance, but answering the question requires an exhaustive search for a training
strategy and model architecture. The analysis conducted in this thesis provides no evidence
that modelling temporal context with YOLOv4 improves traffic light recognition
performance on the DriveU dataset.
Beskrivning
Ämne/nyckelord
object detection; traffic light recognition; recurrent neural networks; temporal context; YOLO