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arXiv · 2607.10605

End-to-End Real-Time Drone-Based Person Detection Framework Using Deep Learning

Payel Sarmah·Ayush Ranjan·Piyush Kaushik Bhattacharyya·Anil Kr. Shaw·Pradip Kr. Das
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Abstract

In recent years, Unmanned Aerial Vehicles (UAVs) or drones have gained rapid response in terms of security, search and rescue (SAR), border surveillance, etc. Existing monitoring frameworks often struggle to maintain detection consistency when targets undergo significant scale variations due to altitude changes, leading to critical information gaps. To address this issue, this work proposes an integrated real-time detection pipeline for detecting targets through the wireless live drone video feed. Build upon YOLOv8-nano architecture, extensive flight experiments were conducted to determine the detection performance across multiple flight altitudes. Trained on VisDrone2019 dataset, the results of YOLOv8-nano model achieves 57.4%, 41%, 44.8% and 20.3% in precision, recall, mAP and mAP50:95 respectively. While demonstrating on real environment, this analysis revealed that the algorithm achieves near-total detection reliability at altitudes between 16 and 25 meters with the detection frame rate consistently maintained above 41 FPS and reaching a peak of 50 FPS. However, the goal of this work is to enable real-time person detection from an aerial platform via wireless transmission. This approach effectively addresses the dual challenges of identifying targets at varying scales and ensuring near-to-accurate localization during aerial observation.

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Payel Sarmah, Ayush Ranjan, Piyush Kaushik Bhattacharyya, Anil Kr. Shaw, Pradip Kr. Das. 2026-07-12. End-to-End Real-Time Drone-Based Person Detection Framework Using Deep Learning. https://arxiv.org/abs/2607.10605

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