Outdoor Image Weather Recognition Based on Image Blocks and Feature Fusion

Abstract

In video surveillance and intelligent traffic,bad weather such as foggy,rainy and snowy can seriously affect the visibility of video images.Therefore,it is very important to quickly identify the current weather conditions and make adaptive clearness processing of surveillance videos.Aiming at the problems of poor effect of traditional weather recognition methods and lack of weather image data sets,a multi-class weather image blocks data set is constructed,and a weather recognition algorithm based on image blocks and feature fusion is proposed.The algorithm uses traditional methods to extract four features,namely average gradient,contrast,saturation and dark channel,which are taken as the shallow features of weather images.The algorithm uses transfer learning to fine -tune the VGG16 pre-training model,and extracts the full-connection layer features of the fine-tuning model,which are taken as the deep features of the weather image.The shallow and deep features of weather images are fused and used as the final features to train the Softmax classifier.The classifier can realize the recognition of foggy,rainy,snowy and sunny wea-ther images. Experimental results show that the recognition accuracy of the proposed algorithm can reach 99.26%,and the algorithm can be used as a weather recognition module in the adaptive video image sharpening system.

Keywords

• image blocks|weather recognition|convolutional neural network|transfer learning|feature extraction|feature fusion