A Neural network approach to visibility range estimation under foggy weather conditions

© 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

Estimating meteorological visibility range under foggy weather conditions: A deep learning approach

© 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

Vehicle Combustion Quality Monitoring:A scene visibility-level based non-invasive approach

Pollutants interfere with light, restrict its reflection and so impair visibility. Scene visibility level is therefore used as a measure of air quality and pollution. Treating emission efflux as "some additional noise causing visibility impairment," this work examines if the extracted visibility index from a thermal infrared (TIR) image can help in qualitative assessment of combustion efficiency. The thin-film regime like two dimensional TIR images of unleaded-petroleum run vehicles' exhaust-plumes were first accommodated for time and space related compositional effects. The estimated ratios of visibility indices obtained from two sequential TIR images of the same exhaust plume were compared with their respective electrochemically sensed levels of oxides of nitrogen and combustibles. Initial results suggest that visibility indices extracted from TIR images of emission efflux would help in distinguishing low from high levels of emissions. TIR images can therefore assist in qualitative assessment of engine combustion efficiency