End-to-End Real-Time Drone-Based Person Detection Framework Using Deep Learning
2026-07-12 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionMachine Learning
AI summaryⓘ
The authors focused on improving drone-based person detection from live video feeds, tackling the problem of targets appearing at different sizes due to changing altitudes. They used a lightweight YOLOv8-nano model trained on a drone-specific dataset and tested it across various flight heights. Their system works best at altitudes between 16 and 25 meters, maintaining high detection reliability and fast processing speeds. This helps ensure that drones can detect people accurately and in real time while flying at different heights.
Unmanned Aerial Vehicles (UAVs)YOLOv8-nanoperson detectionreal-time detectionmAP (mean Average Precision)flight altitudewireless video transmissionVisDrone2019 dataset
Authors
Payel Sarmah, Ayush Ranjan, Piyush Kaushik Bhattacharyya, Anil Kr. Shaw, Pradip Kr. Das
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.