Open AccessFog level estimation using non‐parametric intensity curves in road environments
Author(s) -
Lee Y.,
Kim G.
Publication year - 2017
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2017.1753
Subject(s) - intensity (physics) , pixel , artificial neural network , computer science , convergence (economics) , feature (linguistics) , parametric statistics , artificial intelligence , remote sensing , estimation , computer vision , environmental science , mathematics , statistics , geology , optics , engineering , physics , linguistics , philosophy , economics , economic growth , systems engineering
A fog level estimation method using intensity curves arranged with geometrical information is proposed. The curves, extracted from pixels in the road region and reflecting the tendency of intensity convergence under foggy conditions, are provided to the stacked auto‐encoders and the encoded features are classified into four fog levels by a neural network. Experimental results with clear and foggy road images of various places show that the intensity curve feature of the proposed method is effectively working not only in detecting the presence of fog, but also in estimating the fog level accurately.