Saving Lives from Above: Person Detection in Disaster Response Using Deep Neural Networks

Abstract

This paper focuses on person detection in aerial and drone imagery, which is crucial for various operations such as situational awareness, search and rescue, and safe delivery of supplies. We aim to improve disaster response efforts by enhancing the speed, safety, and effectiveness of the process. Therefore, we introduce a new person detection dataset comprising 311 annotated aerial and drone images, acquired from helicopters and drones in different scenes, including urban and rural areas, and for different scenarios, such as estimation of damage in disaster-affected zones, and search and rescue operations in different countries. The amount of data considered and level of detail of the annotations resulted in a total of 10,050 annotated persons. To detect people in aerial and drone images, we propose a multi-stage training procedure to improve YOLOv3's ability. The proposed procedure aims at addressing challenges such as variations in scenes, scenarios, people poses, as well as image scales and viewing angles. To evaluate the effectiveness of our proposed training procedure, we split our dataset into a training and a test set. The latter includes images acquired during real search and rescue exercises and operations, and is therefore representative for the challenges encountered during operational missions and suitable for an accurate assessment of the proposed models. Experimental results demonstrate the effectiveness of our proposed training procedure, as the model's average precision exhibits a relevant increase with respect to the baseline value.