Source of Publication
Procedia Computer Science
© 2018 The Authors. Published by Elsevier Ltd. 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.
Computer vision, Convolution neural networks, Deep learning, Intelligent transportation systems, Machine learning, Meteorologcal visibility, Neural networks, Visibility distance
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Chaabani, Hazar; Werghi, Naoufel; Kamoun, Faouzi; Taha, Bilal; Outay, Fatma; and Yasar, Ansar Ul Haque, "Estimating meteorological visibility range under foggy weather conditions: A deep learning approach" (2018). All Works. 1531.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series