基于改进卷积神经网络的雾天识别技术OA

An improved convolutional neural network model for fog classification,based on convolutional neural network model fusion,incorporating a weighted attention mechanism,was introduced.Experimental evaluations utilizing fog image datasets from Lufeng,Xichou,Jiangcheng,and Honghe national weather stations in Yunnan Province,yield the following findings:(1)by incorporating a weighted attention mechanism,the enhanced convolutional neural network model demonstrates superior fog image feature extraction capabilities,achieving an average recognition accuracy of 95.69%.This surpasses the accuracy of traditional models like VGG and AlexNet.(2)When the trained model is applied to test fog image data from different time periods,it maintains an accuracy exceeding 90%at all four stations,with minimal identification errors among the test samples.This suggests that the model can serve as a valuable supplement to automatic weather station fog observations.(3)Analysis of erroneous test image cases reveals that the model's generalization ability may be compromised by the absence of certain training data.Additionally,rainfall can lead to misclassifications.Furthermore,the high similarity of image samples at the classification boundaries during foggy conditions can result in incorrect classifications by the model.