Abstract
There have been a significant number of recent studies on autonomous landing in unmanned aerial vehicles (UAVs). Early studies employed a global positioning system (GPS) receivers for this purpose. However, because GPS signals cannot be used in certain urban environments, prior studies used vision-based marker detection. To accurately detect a marker, a high-resolution camera on a drone must obtain a high-quality image. This can not only be expensive but also increases the weight of the drone. In general, drones are only equipped with a frontal-viewing and fixed angle camera, and an additional downward-viewing camera becomes necessary for drone landing. Therefore, expensive and weighted high-resolution cameras are not feasible for use on drones. Nevertheless, most previous studies on vision-based drone landing use high-resolution images. To address such limitations, we propose a new method of drone landing using deep learning-based super-resolution reconstruction and marker detection on an image captured by a cost-effective and low-resolution visible light camera. The experimental results on two datasets demonstrate that our method exhibits higher performance than the existing methods in terms of super-resolution reconstruction and marker detection.
Original language | English |
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Article number | 8710329 |
Pages (from-to) | 61639-61655 |
Number of pages | 17 |
Journal | IEEE Access |
Volume | 7 |
DOIs | |
State | Published - 2019 |
Keywords
- autonomous landing
- deep learning-based marker detection
- deep learning-based super-resolution reconstruction
- Unmanned aerial vehicle
- visible light camera on drone