高速公路雾图像能见度的监测与识别方法研究OA

In recent years,automatic visibility stations have been set up along expressways to monitor fog,which has played an important role in ensuring traffic safety.However,the automatic visibility stations are generally far away from each other,and cannot monitor local fogs and mass fogs in a small range.Therefore,this paper proposes a visibility recognition method based on fog images on expressways.The collected highway fog weather images are preprocessed,and the image features,monitoring factors and interest panes with high correlation with visibility are selected.Machine learning method is also adopted to explore the relationship between image features and visibility in fog weather,a binary linear regression model for visibility in fog weather is constructed,and the output results of the monitoring model is verified.The results show that:(1)through the experiment,it is proved that the mean value of saturation and the variance of chroma have a high correlation with visibility,while the three color features of red,green and blue have a low correlation with visibility,indicating that saturation and chroma are the key factors for visibility monitoring,rather than color.(2)By dividing different visibility levels,the image visibility is determined based on the random forest algorithm,and the classification accuracy of the model reaches 90%,which has a strong classification ability for the determination of the visibility interval of the images.(3)The binary linear regression model with different visibility levels is constructed,and the verification results of the verification data set show that the visibility prediction accuracy of the model is high,and the predicted values are all within the correct range,of which 70%of the predicted values were very close to the true values.