Abstract: Deep learning is a sub field of machine learning that focuses on training artificial neural networks to learn and make predictions or decisions without being explicitly programmed.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.. 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 convolution 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 Efficient DET architecture and ensemble learning. The method has been validated on the HERIDAL image datasets. 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 datasets.
Keywords: Search and Rescue Operations, Unnamed Aerial Vehicles, Heridal Image Dataset, Ensemble learning.
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DOI:
10.17148/IJARCCE.2023.125127
