WeatherEye-Proposal of an Algorithm Able to Classify Weather Conditions from Traffic Camera Images

Atmosphere

Frédéric Bernardin¹  Khouloud Dahmane¹  Michèle Colomb¹  Pierre Duthon¹  Christophe Blanc²  Frédéric Chausse² 

[1] Cerema, Equipe-Projet STI, 8-10, Rue, Bernard Palissy, CEDEX 2, F-63017 Clermont-Ferrand, France;Institut Pascal, Université Clermont Auvergne, BP 10448, F-63000 Clermont-Ferrand, France;

关键词: intelligent transportation systems;    image processing;    artificial intelligence;    machine vision;    cameras;    advanced driver assistance system;   

DOI  :  10.3390/atmos12060717

来源: DOAJ

【 摘 要 】

In road environments, real-time knowledge of local weather conditions is an essential prerequisite for addressing the twin challenges of enhancing road safety and avoiding congestions. Currently, the main means of quantifying weather conditions along a road network requires the installation of meteorological stations. Such stations are costly and must be maintained; however, large numbers of cameras are already installed on the roadside. A new artificial intelligence method that uses road traffic cameras and a convolution neural network to detect weather conditions has, therefore, been proposed. It addresses a clearly defined set of constraints relating to the ability to operate in real-time and to classify the full spectrum of meteorological conditions and order them according to their intensity. The method can differentiate between five weather conditions such as normal (no precipitation), heavy rain, light rain, heavy fog and light fog. The deep-learning method’s training and testing phases were conducted using a new database called the Cerema-AWH (Adverse Weather Highway) database. After several optimisation steps, the proposed method obtained an accuracy of 0.99 for classification.

【 授权许可】

Unknown