University of Twente Student Theses
Automatic labeling of road quality using machine learning
Tazelaar, Steven (2022) Automatic labeling of road quality using machine learning.
|
PDF
1MB |
| Abstract: | The goal of this research is to create a software that uses machine learning to accurately label road quality when cycling to get an overview of the roads that are in need of improvement or are being experienced as a good road. A data collection setup was made, using IMU and GPS sensors connected to a Raspberry Pi with a connection to bucket. The user labels roads as good or bad using two buttons on the bike handlebars. Two machine learning algorithms are applied to the data: LDA and QDA. A validation was performed on a road of which the model did not get a label as input. LDA ad QDA lead to an accuracy of 0.83 and 0.82 respectively. A specificity of 0.66 and 0.72 was reached respectively and both had a sensitivity of 0.92. During the validation, all three types of roads that were measured got a classification that matched their label, validating the model. Further research could improve the model, for instance use of more sensors, more data to feed to the model, application of other machine learning models and other methods of labeling data. The LDA and QDA machine learning algorithms both have the potential to automatically label road quality in cycling. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Computer Science BSc (56964) |
| Link to this item: | https://purl.utwente.nl/essays/91817 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
Show download statistics for this publication
Show download statistics for this publicationRepository Staff Only: item control page