Sparsely Annotated Semantic Segmen tation of Weather-Related Road Surface Conditions
Ladda ner
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2024
Författare
Rumar Karlquist, Johan
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This project aims to build upon a machine learning pipeline for semantic segmentation of road conditions, focusing on classifying weather-affected surfaces such as
dry, wet, slush, snow, and ice. Accurate detection of road surface conditions is
crucial for autonomous driving systems and advanced driver-assistance systems as
it directly influences vehicle control strategies, safety measures, and overall driving
experience. Unlike most research in semantic segmentation, which relies heavily on
densely annotated datasets that require significant manual labor to generate, this
project utilises sparsely annotated data. These sparse labels, though containing
less information, substantially reduce the need for manual annotation. Additionally, the provided data uses soft labels, representing a probability distribution over
class conditions, which differs from the commonly used hard labels representing a
single class. Data collection involves vehicles equipped with a front-facing camera
recording the road and two laser detectors that gathers information about the road
surface conditions.
Two pre-processing approaches were explored: one crops the original input image,
and the other performs an image transformation to simulate a bird’s-eye view of
the road. Multiple new machine learning models were implemented, but it was
observed that the choice of model did not significantly affect performance, indicating
possible limitations in the provided data. Consequently, various approaches for
augmenting data and methods to extract further information from unlabeled pixels
were explored, some of which marginally enhanced performance. The pipeline’s
performance was evaluated using conventional metrics such as accuracy and mean
intersection over union, as well as through visualisation of the resulting semantic
segmentation.
Beskrivning
Ämne/nyckelord
Machine learning, Computer vision, Semantic segmentation, Road climatology, Neural networks, Python