基于ResNet卷积神经网络的机场能见度预测

Airport Visibility Prediction Based on ResNet Convolutional Neural Network

机场能见度是飞机安全运行的重要保障。本文选用ResNet卷积神经网络提取机场视频数据的特征向量,并添加多层感知器(MLP)分类提取的特征向量,充分挖掘视频中的时空信息,以增强能见度的分类效果。结果表明,该模型对机场能见度的预测精度达到80.3%,可用于处理类似机场能见度预测的情景。

Visibility is an important guarantee for the safe operation of aircraft.This paper uses ResNet convolutional neural network to extract feature vectors from airport video data,and adds multi-layer perceptron(MLP) to classify the extracted feature vectors,fully mining the spatiotemporal information in the video to improve the classification effect of visibility.The results show that the prediction accuracy of the model reaches 80.3%,which can be used to deal with similar scenarios of airport visibility prediction.