红外技术2025，Vol.47Issue(12)：1468-1482,15.
基于YOLO网络的无人机红外目标检测研究进展
Research Progress on UAV Infrared Target Detection Based on YOLO
摘要
Abstract
Infrared target detection on unmanned aerial vehicles(UAVs)has become a key capability for intelligent perception and autonomous decision-making in public security,border surveillance,and emergency response.By leveraging end-to-end architectures and strong feature-learning efficiency,the YOLO family of neural models has surpassed traditional handcrafted methods and now represents the mainstream framework for infrared detection.Recent progress in anchor-free design,multi-scale fusion,attention mechanisms,and end-to-end inference has markedly improved the detection of small and low-contrast targets in complex scenes.In this study,we review UAV-based infrared detection approaches built on YOLO models,synthesize major enhancement strategies,and evaluate their effects on weak target recognition and real-time performance while summarizing representative datasets.Remaining challenges such as retention of weak signal,cross-modal alignment,and spatiotemporal modeling are analyzed,and future directions toward multi-source collaborative perception and onboard intelligent deployment are outlined.关键词
无人机/目标检测/红外图像/计算机视觉/深度学习Key words
UAV/object detection/infrared images/computer vision/deep learning分类
信息技术与安全科学引用本文复制引用
YANG Haitao,YAN Zhiyuan,JIANG Zihan,WANG Huapeng,HAN Kai,YAN Xiai,XIONG Yingzhuo,ZHU Mingzhe..基于YOLO网络的无人机红外目标检测研究进展[J].红外技术,2025,47(12):1468-1482,15.基金项目
湖南省重点研发计划(2024AQ2023,2024AQ2024) （2024AQ2023,2024AQ2024）
湖南省社会科学评审委员会课题(XSP2025WTY003) （XSP2025WTY003）
湖南省教育厅重点项目(23A0705) （23A0705）
重庆市自然科学基金创新发展联合基金(CSTB2022NSCQ-LZX007) （CSTB2022NSCQ-LZX007）
湖南省生态环境科研项目(HBKYXM-2024034). （HBKYXM-2024034）
