Abstract
Nowadays, complex image processing algorithms are a necessity to make UAVs more autonomous. Currently, the processing of images of the on-board camera is often performed on a ground station, thus severely limiting the operating range. On-board processing has numerous advantages, however determining a good trade-off between speed, power consumption and weight of a specific hardware platform for on-board processing is hard. Many hardware platforms exist, and finding the most suited one for a specific vision algorithm is difficult. We present a framework that automatically determines the most-suited hardware platform given an arbitrary complex vision algorithm. Our framework estimates the speed, power consumption and flight time of this algorithm for multiple hardware platforms on a specific UAV. We demonstrate this methodology on two real-life cases and give an overview of the present top performing CPU-based platforms for on-board UAV image processing.
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This work is funded by KU Leuven via the CAMETRON project.
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Hulens, D., Verbeke, J., Goedemé, T. (2016). Choosing the Best Embedded Processing Platform for On-Board UAV Image Processing. In: Braz, J., et al. Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2015. Communications in Computer and Information Science, vol 598. Springer, Cham. https://doi.org/10.1007/978-3-319-29971-6_24
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DOI: https://doi.org/10.1007/978-3-319-29971-6_24
Publisher Name: Springer, Cham
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