Year: 2014
Type: dissertation
Abstract: The objective of this project is to design a lane type recognition system for personal mobility vehicles in the city, to increase the safety of users of this type of vehicle, generate possible data and statistics useful for future studies and study the feasibility and future of this type of technology in this field. The system is based on a Deep Learning model trained with previously captured data. The training of the model was implemented with a large number of training, validation and test images extracted from various videos recorded on the city during the first weeks of the project. The results of the first training sessions were correct, with some possible upgrades, which after being applied allowed to obtain a system with an excellent F1-Score and high precision for all the different classes to be identified. On the other hand, the performance and precision observed in the script that uses the trained model to detect the type of road in any video introduced, are really good, and obviating certain aspects that could be improved (like some detection errors) by training more times the model if we had more data, they are indicators of the usefulness and help that this type of technology can bring to personal mobility vehicles in cities.?
Author: J.J. González‐López
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3132669614