Sky Region Detection in a Single Image for Autonomous Ground Robot Navigation

Abstract

The sky region in an image provides horizontal and background information for autonomous ground robots and is important for vision-based autonomous ground robot navigation. This paper proposes a sky region detection algorithm within a single image based on gradient information and energy function optimization. Unlike most existing methods, the proposed algorithm is applicable to both colour and greyscale images. Firstly, the gradient information of the image is obtained. Then, the optimal segmentation threshold in the gradient domain is calculated according to the energy function optimization and the preliminary sky region is estimated. Finally, a post-processing method is applied in order to refine the preliminary sky region detection result when no sky region appears in the image or when objects extrude from the ground. Experimental results have proven that the detection accuracy is greater than 95% in our test set with 1,000 images, while the processing time is about 150ms for an image with a resolution of 640×480 on a modern laptop using only a single core.

Sky segmentation with ultraviolet images can be used for navigation

“…The level of accuracy obtained is comparable with that reported in other state of the art sky segmentation methods, e.g. 96.05% reported in [20]. In fact a proportion of our remaining error was simply due to vignetting of the overlapping lens hood (see Fig.…”

“…[25], can be used to label sky amongst other regions, but the most successful current methods depend on advanced machine learning methods over large numbers of sample images to estimate the characteristics of sky. A range of approaches specific to detecting the sky region only are summarised in [20]. These include combining colour and texture characteristics, modelling the colour gradient, using connectedness or picture region constraints, and using alternative colour spaces and classification algorithms such as support vector machines.…”

“…These include combining colour and texture characteristics, modelling the colour gradient, using connectedness or picture region constraints, and using alternative colour spaces and classification algorithms such as support vector machines. Shen and Wang [20] propose a greyscale method that uses gradient information and energy function maximisation, combined with several picture region assumptions, to obtain results on the order of 95-96% correct classification.…”

Underwater image and video dehazing with pure haze region segmentation

“…The DCP makes the assumption that information in the darkest channel of a hazy image can be attributed to the haze ( He et al, 2011 ). Terrestrial dehazing methods detected sky regions in still images with features such as the saturation/brightness ratio, intensity variance and magnitude of edges ( Rankin et al, 2011 ), gradient information and an energy function optimisation ( Shen and Wang, 2013 ), and semantic segmentation ( Wang et al, 2014 ). A method for image and video dehazing suppressed the generation of visual artefacts in sky regions by minimising the residual of the gradients between the input image and the dehazed output ( Chen et al, 2016 ).…”

Cloud-Edge Suppression for Visual Outdoor Navigation

“…However, these approaches need additional sensors and are therefore not suitable for applications relying on standard color cameras. In [47], an algorithm based on energy function optimization is proposed, which separates sky and ground by only using a standard color camera. This algorithm successfully separates the ground and sky region in the presence of relatively simple and smooth sky region borders.…”