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Descripción
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| Vision-based sky segmentation and horizon line detection can be extremely useful to perform important tasks onboard Unmanned Aerial Vehicles (UAVs), such as pose estimation and collision avoidance. Most of the existing vision-based solutions use traditional image processing methods to identify the horizon line. This results in good overall accuracy and fast computation times. However, difficult environmental conditions such as a foggy or cloudy skies hinder correct sky segmentation. This paper proposes a solution for sky segmentation in RGB images using a supervised Machine Learning approach by first splitting the image into fixed-size patches, extracting and classifying color descriptors for each patch and performing a final post-processing stage to improve segmentation quality. A method for automatic horizon line detection is also proposed. The performance of our approach was evaluated on flight images captured onboard UAVs, achieving performance accuracies above 93% at real-time frame rates. | |
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Internacional
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Si |
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Nombre congreso
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International Conference on Unmanned Aircraft Systems (ICUAS), 2016 |
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Tipo de participación
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960 |
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Lugar del congreso
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Arlington, VA USA |
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Revisores
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Si |
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ISBN o ISSN
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978-1-4673-9334-8 |
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DOI
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Fecha inicio congreso
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07/06/2016 |
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Fecha fin congreso
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10/06/2016 |
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Desde la página
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8 |
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Hasta la página
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14 |
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Título de las actas
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A Real-time Supervised Learning Approach for Sky Segmentation Onboard Unmanned Aerial Vehicles |