期刊
出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.procs.2018.10.139
关键词
Visibility distance; intelligent transportation systems; meteorologcal visibility; neural networks; deep learning; convolution neural networks; machine learning; computer vision
资金
- Zayed University Research Incentive Fund (RIF) [R16075]
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.
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