Detecting Humans in Search and Rescue Operations Based on Ensemble Learning

Abstract

Detection of humans accurately in aerial images is critical for various applications such as surveillance, detecting and tracking athletes on sports fields, and search and rescue operations (SAR). The goal of SAR is to assist, detect, and rescue people who have had accidents in the mountains or other hazardous environments. By using drones in SAR applications, it is desirable to minimize the cost and time spent on SAR operations. In this paper, we present a convolutional neural network-based model for the detection of humans in aerial images of mountain landscapes acquired by unmanned aerial vehicles (UAVs) used in search and rescue operations. Detection of humans in aerial images remains a complex task due to various challenges such as pose and scale variations of humans, low visibility, camouflaged environment, adverse weather conditions, motion blur, and high-resolution aerial images. Due to imaging from high altitudes, in most high-resolution aerial images captured by UAVs, only 0.1 to 0.2 percentage of the image represents humans. To solve the problem of low coverage of the object of interest in high-resolution aerial images, we propose to implement a deep learning-based object detection model. In this paper, we propose a novel method for the detection of humans in aerial images based on the EfficientDET architecture and ensemble learning. The method has been validated on the HERIDAL image dataset. By implementing the proposed methodologies, we achieved an mAP of 95.11%. To the best of our knowledge, this is the highest accuracy result for human detection on the HERIDAL dataset.