Name: LUCAS TABELINI TORRES
Publication date: 31/07/2023
Examining board:
Name |
Role |
|---|---|
| THIAGO OLIVEIRA DOS SANTOS | Advisor |
Summary: The lane detection problem has been extensively researched in the past decades, especially since the
advent of deep learning. Despite the numerous works proposing solutions to the localization task (i.e., localizing the
lane boundaries in an input image), the classification task has not seen the same focus. Nonetheless, knowing the
type of lane boundary, particularly that of the ego lane, can be very useful for many applications. For instance, a vehicle might not be allowed by law to overtake depending on the type of the ego lane. Beyond that, very few works
take advantage of the temporal information available in the videos captured by the vehicles: most methods employ a
single-frame approach. In this work, building upon the recent deep learning-based model LaneATT, we propose an
approach to exploit the temporal information and integrate the classification task into the model. This is
accomplished by extracting features from multiple frames using a deep neural network (instead of only one as in
LaneATT). Our results show that the proposed modifications can improve the detection performance on the most
recent benchmark by 2.34%, establishing a new state-of-the-art. Finally, an extensive evaluation shows that it enables
a high classification performance (89.37%) that serves as a future benchmark for the field.
