1 min read Research

A fusion between a deep-learning and a filter-based approach for road marker detection

Fusing the outputs of a supervised deep-learning and an unsupervised filter-based road marker detector into a new supervised model, reaching 95% accuracy.
Title slide of the BSc thesis presentation

By Jeroen G. S. Overschie. Bachelor's thesis, University of Groningen, supervised by dr. G. Azzopardi, in assignment for Kaios.ai. Published 23 August 2019.

Code · PDF

Abstract

In this study, a fusion between two existing algorithms for detecting road markings is proposed. Considered are two existing algorithms, one supervised, deep-learning algorithm, and another unsupervised filter-based algorithm. The proposed fusion algorithm takes the output images of both approaches in order to train a new model using supervised learning. Under different conditions of downscale factor, classifier and feature-vector composition, the fusion algorithm managed to obtain an accuracy level of 95%.

GitHub - dunnkers/bsc-thesis: BSc Thesis - A fusion between a deep-learning and a filter-based approach for road marker detection
BSc Thesis - A fusion between a deep-learning and a filter-based approach for road marker detection - dunnkers/bsc-thesis
GitHub

Published by:

Jeroen Overschie

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