Road condition classification from CCTV images using machine learning
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Engineering mathematics and computational science (MPENM), MSc
Publicerad
2023
Författare
Askbom, Ludvig
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Understanding and categorizing road conditions is crucial for driver safety and road
maintenance. This research explores practical approaches to classify road conditions
using images from CCTV stations. Two classification challenges are addressed:
distinguishing between snowy and non-snowy conditions and between snowy, wet,
and dry conditions.
The thesis evaluates various machine learning methods for road condition classification
on multiple CCTV stations, including established and novel approaches.
Established methods involve feature extraction through texture analysis and finetuning
convolutional neural networks and vision transformers. Novel contributions
include training an image segmentation model for road segmentation and utilizing
persistent homology for feature extraction. Notably, this thesis sets itself apart by
separating data into training and test sets based on CCTV stations. This is important
to evaluate the methods’ and models’ abilities to generalize to new CCTV
stations.
The best-performing model, a fine-tuned vision transformer, achieved accuracies
of 87.9% and 75.3% for classifying snow/no snow and snow/wet/dry, respectively.
These results underscore the complexity of the classification problem and highlight
the effectiveness of deep learning models for large-scale road condition classification
based on images.
Beskrivning
Ämne/nyckelord
Road condition, classification, machine learning, deep learning, feature extraction, vision transformer