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Offloading Deep Learning Empowered Image Segmentation from UAV to Edge Server
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DOI：10.1109/TSP52935.2021.9522611.png)
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Image and video analysis in unmanned aerial vehicle (UAV) systems have been a recent interest in many applications since the images taken by UAV systems can provide useful information in many domains including maintenance, surveillance and entertainment. However, a constraint on UAVs is having limited battery power and recent developments in the artificial intelligence (AI) domain encourages many applications to run computationally heavy algorithms on the taken UAV images. Such applications drain the power from the on-board battery rapidly, while requiring strong computationally strong resources. An alternative to that approach is offloading heavy tasks such as object segmentation to a remote (edge) server and perform the heavy computation on that server. However, the effect of the communication system and the used channel introduce noise on the transferred data and the effect of the noise due to the use of such LTE communication system on pre-trained deep networks has not been previously studied in the literature. In this paper, we study one such scenario where the images taken by UAVs and (the same images) transferred to an edge server via an LTE communication system under different scenarios. In our case, the edge server runs an off-the-shelf pretrained deep learning algorithm to segment the transmitted image. We provide an analysis of the effect of the wireless channel and the communication system on the final segmentation of the transmitted image on such a scenario.
Keyword:
Computational offloading
image segmentation
edge computing
UAV image processing
deep learning
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