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Drone-based person detection framework uses YOLOv8-nano for real-time surveillance

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a real-time person detection framework for drones using deep learning, specifically the YOLOv8-nano architecture. This system aims to overcome challenges in maintaining detection consistency across varying target scales due to drone altitude changes. Trained on the VisDrone2019 dataset, the YOLOv8-nano model achieved notable precision and recall rates, demonstrating high detection reliability between 16 and 25 meters altitude with frame rates exceeding 41 FPS. AI

IMPACT This framework could enhance real-time surveillance and search-and-rescue operations by improving drone-based object detection capabilities.

RANK_REASON The item is an academic paper detailing a new deep learning framework for drone-based person detection. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Drone-based person detection framework uses YOLOv8-nano for real-time surveillance

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The item is an academic paper detailing a new deep learning framework for drone-based person detection. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Payel Sarmah, Ayush Ranjan, Piyush Kaushik Bhattacharyya, Anil Kr. Shaw, Pradip Kr. Das ·

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

    arXiv:2607.10605v1 Announce Type: cross 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 con…

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