Authors:
Sasa Sambolek
1
and
Marina Ivasic-Kos
2
Affiliations:
1
High school Tina Ujevica, Kutina, Croatia
;
2
Faculty of Informatics and Digital Technologies, University of Rijeka and Centre for Artificial Intelligence University of Rijeka, Rijeka, Croatia
Keyword(s):
Drone Imagery, Deep Learning, Person Detection, YOLOv8, Search and Rescue.
Abstract:
The use of drones in SAR operations has become essential to assist in the search and rescue of a missing or injured person, as it reduces search time and costs, and increases the surveillance area and safety of the rescue team. Detecting people in aerial images is a demanding and tedious task for trained humans as well as for detection algorithms due to variations in pose, occlusion, scale, size, and location where a person may be in the image, as well as poor shooting conditions, poor visibility, blur due to movement and the like. In this paper, the YOLOv8 generic object detection model pre-trained on the COCO dataset is fine-tuned on the customized SARD dataset used to optimize the model for person detection on aerial images of mountainous landscapes, which are captured by drone. Different models of the YOLOv8 family algorithms fine-tuned on the SARD set were experimentally tested and it was shown that the YOLOv8x model achieves the highest mean average precision (mAP@0.5:0.95) of
63.8%, with an inference time of 4.6 ms which shows potential for real-time use in SARD operations. We have tested three geolocation algorithms in real conditions and proposed modification and recommendations for using in SAR missions for determining the geolocation of a person recorded by drone after automatic detection with the YOLOv8x model.
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