Rethinking Drone-Based Search and Rescue with Aerial Person Detection
- URL: http://arxiv.org/abs/2111.09406v1
- Date: Wed, 17 Nov 2021 21:48:31 GMT
- Title: Rethinking Drone-Based Search and Rescue with Aerial Person Detection
- Authors: Pasi Pyrr\"o, Hassan Naseri, Alexander Jung
- Abstract summary: The visual inspection of aerial drone footage is an integral part of land search and rescue (SAR) operations today.
We propose a novel deep learning algorithm to automate this aerial person detection (APD) task.
We present the novel Aerial Inspection RetinaNet (AIR) algorithm as the combination of these contributions.
- Score: 79.76669658740902
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The visual inspection of aerial drone footage is an integral part of land
search and rescue (SAR) operations today. Since this inspection is a slow,
tedious and error-prone job for humans, we propose a novel deep learning
algorithm to automate this aerial person detection (APD) task. We experiment
with model architecture selection, online data augmentation, transfer learning,
image tiling and several other techniques to improve the test performance of
our method. We present the novel Aerial Inspection RetinaNet (AIR) algorithm as
the combination of these contributions. The AIR detector demonstrates
state-of-the-art performance on a commonly used SAR test data set in terms of
both precision (~21 percentage point increase) and speed. In addition, we
provide a new formal definition for the APD problem in SAR missions. That is,
we propose a novel evaluation scheme that ranks detectors in terms of
real-world SAR localization requirements. Finally, we propose a novel
postprocessing method for robust, approximate object localization: the merging
of overlapping bounding boxes (MOB) algorithm. This final processing stage used
in the AIR detector significantly improves its performance and usability in the
face of real-world aerial SAR missions.
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