Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/28893
Title: Estimating meteorological visibility range under foggy weather conditions: A deep learning approach
Authors: Chaabani, Hazar
Werghi, Naoufel
Kamoun, Faouzi
Taha, Bilal
Outay, Fatma
YASAR, Ansar 
Issue Date: 2018
Publisher: ELSEVIER SCIENCE BV
Source: Shakshuki, E Yasar, A (Ed.). 9TH INTERNATIONAL CONFERENCE ON EMERGING UBIQUITOUS SYSTEMS AND PERVASIVE NETWORKS (EUSPN-2018) / 8TH INTERNATIONAL CONFERENCE ON CURRENT AND FUTURE TRENDS OF INFORMATION AND COMMUNICATION TECHNOLOGIES IN HEALTHCARE (ICTH-2018), ELSEVIER SCIENCE BV,p. 478-483
Series/Report: Procedia Computer Science
Abstract: Systems capable of estimating visibility distances under foggy weather conditions are extremely useful for next-generation cooperative situational awareness and collision avoidance systems. In this paper, we present a brief review of noticeable approaches for determining visibility distance under foggy weather conditions. We then propose a novel approach based on the combination of a deep learning method for feature extraction and an SVM classifier. We present a quantitative evaluation of the proposed solution and show that our approach provides better performance results compared to an earlier approach that was based on the combination of an ANN model and a set of global feature descriptors. Our experimental results show that the proposed solution presents very promising results in support for next-generation situational awareness and cooperative collision avoidance systems. Hence it can potentially contribute towards safer driving conditions in the presence of fog. (C) 2018 The Authors. Published by Elsevier Ltd.
Notes: [Chaabani, Hazar; Kamoun, Faouzi] ESPRIT Sch Engn, ZI Chotrana 2,POB 160, Tunis, Tunisia. [Werghi, Naoufel; Taha, Bilal] Khalifa Univ, POB 127788, Abu Dhabi, U Arab Emirates. [Outay, Fatma] Zayed Univ, POB 19282, Dubai, U Arab Emirates. [Yasar, Ansar-Ul-Haque] Hasselt Univ, Wetenschapspk 5 Bus 6,POB 3590, Diepenbeek, Belgium.
Keywords: Visibility distance; intelligent transportation systems; meteorologcal visibility; neural networks; deep learning; convolution neural networks; machine learning; computer vision;Visibility distance; intelligent transportation systems; meteorologcal visibility; neural networks; deep learning, convolution neural networks; machine learning; computer vision.
Document URI: http://hdl.handle.net/1942/28893
DOI: 10.1016/j.procs.2018.10.139
ISI #: 000471261700062
Rights: 2018 The Authors. Published by Elsevier Ltd.This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)Selection and peer-review under responsibility of the scientific committee of EUSPN 2018.
Category: C1
Type: Proceedings Paper
Validations: ecoom 2020
Appears in Collections:Research publications

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