使用深度学习的基于无人机的端到端实时人体检测框架

中文摘要 AI 辅助

近年来，无人机在安全、搜救、边境监测等方面得到快速应用。现有监测框架在目标因高度变化产生显著尺度变化时，难以保持检测一致性，导致关键信息缺失。为此，本文提出通过无线实时无人机视频流检测目标的集成实时检测管道。基于YOLOv8 - nano架构，进行大量飞行实验以确定多飞行高度下的检测性能。在VisDrone2019数据集上训练，YOLOv8 - nano模型的精度、召回率、mAP和mAP50:95分别达到57.4%、41%、44.8%和20.3%。在实际环境演示中，该算法在16至25米高度实现近完全检测可靠性，检测帧率持续高于41 FPS，峰值达50 FPS。此方法有效应对了不同尺度目标识别和空中观察时近精确定位的双重挑战。

英文摘要

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.

发表机构

• Centre for Drone Technology（无人机技术中心）
• Indian Institute of Technology Guwahati（印度理工学院古瓦哈提分校）
• Dept. of CSE（计算机科学与工程系）
• KIIT University（KIIT大学）
• Drone LAB（无人机实验室）
• National Institute of Electronics and Information Technology（电子与信息技术国家研究所）

机构由 AI 辅助整理，请以论文原文为准。