Abstract
Sky detection involves detecting the pixels in an image or video that corresponds to the sky. Horizontal and background information as well vision-based autonomous ground robot navigation is obtained from the sky region in an image. For sky detection, various existing methods are being used. These include techniques of computer vision, probability models, and a number of different machine learning algorithms. Various parameters affect the accuracy of a sky detection algorithm. One major factor is haze or smog in the sky. Because of haze, dust, smoke, and other dry particulates conceal the clarity of the sky. Some other factors include time of the day, weather, and season. Hence, by taking these factors into consideration, this paper aims at building a model that detects the sky in the image that was taken. The model is developed using existing algorithms of machine learning.
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Sahoo, D.K., Lobo, J., Pradhan, S., Rajguru, S., Rakhi, K. (2023). Sky Detection in Outdoor Spaces. In: Misra, R., et al. Advances in Data Science and Artificial Intelligence. ICDSAI 2022. Springer Proceedings in Mathematics & Statistics, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-031-16178-0_1
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DOI: https://doi.org/10.1007/978-3-031-16178-0_1
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