Song Yingchao, Luo Haibo, Ma Junkai, Hui Bin, Chang Zheng
Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2018 Apr 1;18(4):1060. doi: 10.3390/s18041060.
Sky detection plays an essential role in various computer vision applications. Most existing sky detection approaches, being trained on ideal dataset, may lose efficacy when facing unfavorable conditions like the effects of weather and lighting conditions. In this paper, a novel algorithm for sky detection in hazy images is proposed from the perspective of probing the density of haze. We address the problem by an image segmentation and a region-level classification. To characterize the sky of hazy scenes, we unprecedentedly introduce several haze-relevant features that reflect the perceptual hazy density and the scene depth. Based on these features, the sky is separated by two imbalance SVM classifiers and a similarity measurement. Moreover, a sky dataset (named HazySky) with 500 annotated hazy images is built for model training and performance evaluation. To evaluate the performance of our method, we conducted extensive experiments both on our HazySky dataset and the SkyFinder dataset. The results demonstrate that our method performs better on the detection accuracy than previous methods, not only under hazy scenes, but also under other weather conditions.
天空检测在各种计算机视觉应用中起着至关重要的作用。大多数现有的天空检测方法是在理想数据集上训练的，当面对诸如天气和光照条件影响等不利情况时可能会失效。本文从探测雾霾密度的角度提出了一种用于雾霾图像中天空检测的新算法。我们通过图像分割和区域级分类来解决这个问题。为了表征雾霾场景中的天空，我们前所未有地引入了几个与雾霾相关的特征，这些特征反映了感知到的雾霾密度和场景深度。基于这些特征，通过两个不平衡支持向量机分类器和一个相似度测量来分离天空。此外，还构建了一个包含500张带注释的雾霾图像的天空数据集（名为HazySky）用于模型训练和性能评估。为了评估我们方法的性能，我们在HazySky数据集和SkyFinder数据集上都进行了广泛的实验。结果表明，我们的方法不仅在雾霾场景下，而且在其他天气条件下，在检测准确率方面都比以前的方法表现更好。
