A Fog Level Detection Method Based on Grayscale Features
Cong Li, Xiaobo Lu, Tong Chen, Weili Zeng · 2014
With the rapid development of highway, the distribution of surveillance cameras has become increasingly intensive, which brings important significance to traffic safety by detecting visibility of fog using surveillance video. In this paper, a fog level detection method based on grayscale features is proposed. Sometimes there is no proper calibration template in highway. In order to meet the requirement of transport regulation on visibility, we classify fog level qualitatively into big fog, little fog and no fog by analyzing the change of average gray value with the ordinate in different weather conditions. The experiment results show that this method classifies accurately, quickly and is widely applicable.