In hazy weather,visibility detection methods based on image processing are still under continuous research,and the accuracy of visibility estimation is dependent on the accuracy of visibility estimation There is room for improvement.Based on big data,this paper improves VGG convolutional neural network to extract features of video data and uses Adam for algorithm optimization to fully mine surveillance video data information,so as to achieve the purpose of improving accuracy and reducing equipment cost.Compared with ResNet,this method makes full use of the spatio-temporal information of video data,and shows higher precision and accuracy in the prediction process.This study provides a reference for improving the effectiveness of airport visibility prediction.
visibility forecastconvolutional neural networkairport surveillance video
万方数据
维普
