Enhancing Post-Disaster Survivor Detection Using UAV Imagery and Transfer Learning Strategies

Abstract

In disaster response and search and rescue operations, the immediate detection of survivors remains a critical challenge. This research pioneers a novel approach to rapidly detect survivors in disaster areas using UAVs and advanced computer vision techniques. Leveraging the YOLOv8 object detection model, the study explores how synthetic and real disaster-specific datasets, alongside transfer learning, enhance survivor detection capabilities. Results show promising adaptability, with the YOLOv8 model achieving an average precision (AP) of 0.864, marking a significant 32% improvement over the previous state-of-the-art (SOTA) performance of 0.654 achieved with a slower model. Furthermore, the combination of fine-tuning a pre-trained model on the newly built dataset surpassed, by a small margin, even the standard training method despite utilizing only half the number of epochs. Additionally, this paper proposes a UAV-based system model that integrates computer vision for rapid onsite detection, potentially revolutionizing disaster response frameworks. This paper highlights the potential of UAV technology and transfer learning in improving disaster management and guides future investigations in this critical field.