基于图像分块与特征融合的户外图像天气识别

在视频监控及智能交通等领域,雾、雨、雪等恶劣天气会严重影响视频图像能见度,因此快速识别出当前的天气情况,并自适应地对监控视频进行清晰化处理极为重要.针对传统天气识别方法效果差以及天气图像数据集缺乏的问题,构建了一个多类别天气图像分块数据集,并提出了一种基于图像分块与特征融合的天气识别算法.该算法基于传统方法提取平均梯度...

Outdoor Image Weather Recognition Based on Image Blocks and Feature Fusion

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 VGG16pre-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 weather 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.