计算机科学 ›› 2022, Vol. 49 ›› Issue (3): 197-203.doi: 10.11896/jsjkx.201200263
左杰格, 柳晓鸣, 蔡兵
ZUO Jie-ge, LIU Xiao-ming, CAI Bing
摘要： 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、对比度、饱和度、暗通道4种特征作为天气图像的浅层特征,基于迁移学习对VGG16预训练模型进行微调,提取微调模型的全连接层特征作为天气图像的深层特征,将天气图像浅层特征与深层特征融合作为最终特征用于训练Softmax分类器,实现对雾、雨、雪、晴4类天气图像的识别。实验结果表明,所提算法能达到99.26%的识别准确率,并且可作为天气识别模块应用于自适应视频图像清晰化处理系统。
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