Source of Publication
Procedia Computer Science
© 2017 The Authors. Published by Elsevier B.V. The degradation of visibility due to foggy weather conditions is a common trigger for road accidents and, as a result, there has been a growing interest to develop intelligent fog detection and visibility range estimation systems. In this contribution, we provide a brief overview of the state-of-the-art contributions in relation to estimating visibility distance under foggy weather conditions. We then present a neural network approach for estimating visibility distances using a camera that can be fixed to a roadside unit (RSU) or mounted onboard a moving vehicle. We evaluate the proposed solution using a diverse set of images under various fog density scenarios. Our approach shows very promising results that outperform the classical method of estimating the maximum distance at which a selected target can be seen. The originality of the approach stems from the usage of a single camera and a neural network learning phase based on a hybrid global feature descriptor. The proposed method can be applied to support next-generation cooperative hazard & incident warning systems based on I2V, I2I and V2V communications. Peer-review under responsibility of the Conference Program Chairs.
computer vision, driving assistance, fog detection, Fourier Transform, intelligent transportation systems, Koschmieder Law, 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; Kamoun, Faouzi; Bargaoui, Hichem; Outay, Fatma; and Yasar, Ansar Ul Haque, "A Neural network approach to visibility range estimation under foggy weather conditions" (2017). All Works. 175.
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Open Access Type
Gold: This publication is openly available in an open access journal/series