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| 下载次数 | 被引频次 | 阅读次数 |
随着低空经济的快速发展，无人机在物流、安防、应急救援等领域的应用日益广泛，而无人机目标检测技术作为维护低空交通秩序、保障公共安全的核心支撑，具有重要的研究和应用价值。介绍了无人机红外目标检测技术的研究背景和意义；详细剖析了传统算法与基于深度学习算法的原理、步骤及各自的优劣；对无人机红外目标检测的关键技术进行了分析，包括小目标检测技术、复杂背景适应技术、实时性优化技术和轻量级模型设计；探讨了无人机红外目标检测技术所面临的核心挑战及未来发展趋势，为低空经济场景下的算法选型及工程部署提供了有益参考。
Abstract：With the rapid development of low-altitude economy, the applications of unmanned aerial vehicles in the fields of logistics, security, and emergency rescue are becoming increasingly widespread.As the core support for maintaining low-altitude traffic order and ensuring public safety, target detection techniques for unmanned aerial vehicles are of important research value.First, the research background and significance of infrared target detection techniques for unmanned aerial vehicles are introduced.Then, the principles, steps, as well as advantages and disadvantages of traditional algorithms and deep learning-based algorithms are analyzed in detail.Furthermore, the key technologies of infrared target detection for unmanned aerial vehicles are analyzed, including small target detection, complex background adaptation, real-time optimization technology, and lightweight model design.Lastly, the core challenges and future development trends of infrared target detection techniques for unmanned aerial vehicles are discussed, providing useful reference for algorithm selection and engineering deployment in low-altitude economic scenarios.
[1] 李琼，考月英，张莹，等.面向无人机航拍图像的目标检测研究综述[J].图学学报，2024,45(6):1145-1164.
[2] 崔勇强，周威任，毛智鹏，等.基于智能视觉检测与语义分析的反无人机实时预警系统[J/OL].南京信息工程大学学报，1-10[2025-06-21].https://doi.org/10.13878/j.cnki.jnuist.20250402005.
[3] 胡泉，胡运优，孟宪猛，等.基于偏振特征重构与局部差异度量的空中红外目标检测[J/OL].计算机工程，1-13[2025-06-10].https://doi.org/10.19678/j.issn.1000-3428.0252022.
[4] 徐新昊，王俊，王峰，等.融合密集连接与多注意力机制的星载红外遥感图像超分辨率网络[J].红外与毫米波学报，2025,44(2):265-276.
[5] 王长龙，冀鲸宇，赵月飞，等.无人机遥感图像目标检测技术研究综述[J].陆军工程大学学报，2025,4(1):35-46.
[6] UIJLINGS J R R,VAN DE SANDE K E A V D,GEVERS T,et al.Selective Search for Object Recognition[J].International Journal of Computer Vision,2013,104:154-171.
[7] XIA Y L,WU C,YANG X M.L3former:Enhanced Multi-scale Shared Transformer with Local Linear Layer for Long-term Series Forecasting[J].Information Fusion,2025,124:103398.
[8] REN S Q,HE K M,GIRSHICK R,et al.Faster R-CNN:Towards Real-time Object Detection with Region Proposal Networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(6):1137-1149.
[9] ZHANG D Y,XU C Z,CHEN J X,et al.YOLO-DC:Integrating Deformable Convolution and Contextual Fusion for High-performance Object Detection[J].Signal Processing:Image Communication,2025,138:117373.
[10] 孙忠良.大区域(城市)低空安全管控应用发展与未来展望[J].中国安防，2025(Z1):100-103.
[11] 王殿伟，张新，房杰，等.一种无人机航拍图像火灾烟雾检测算法[J].西安邮电大学学报，2025,30(2):66-76.
[12] 罗旭鸿，刘永春，楚国铭，等.基于改进YOLOv5无人机图像目标检测算法[J].无线电工程，2023,53(7):1528-1535.
[13] HE B S,WANG Z H.Aerial Infrared Small Target Detection Algorithm Based on Structure Tensor Screening and Local Contrast Analysis[J].Infrared Technology,2023,45(11):1169-1176.
[14] 马鹏阁，王招鹏，王江南，等.基于FACET滤波加权局部对比度的红外小目标检测[J].电光与控制，2024,31(12):27-32.
[15] 马鹏阁，魏宏光，孙俊灵，等.基于高斯-拉普拉斯滤波的增强局部对比度红外小目标检测算法[J].兵工学报，2023,44(4):1041-1049.
[16] DUAN C P,HU B L,LIU W,et al.Infrared Small Target Detection Method Based on Frequency Domain Clutter Suppression and Spatial Feature Extraction[J].IEEE Access,2023,11:85549-85560.
[17] 陈海永，刘登斌，晏行伟.基于IDOU-YOLO的红外图像无人机目标检测算法[J].应用光学，2024,45(4):723-731.
[18] WANG Z,WANG J X,WANG Y R,et al.ECO-SWGST:ECO-based Tracking with Side Window Generalized Structure Tensor for Real-time Infrared Small Target Tracking[J].Optics & Laser Technology,2025,192:113470.
[19] 陈华杰，钱逸凡，龙翔，等.基于数据驱动的红外弱小机动目标检测与跟踪[J].电光与控制，2024,31(12):64-71,105.
[20] 李琦，席建祥，杨小冈，等.联合图卷积和聚类的红外无人机集群多目标跟踪算法[J].电光与控制，2025,32(3):15-20.
[21] 张汝榛，张建林，祁小平，等.复杂场景下的红外目标检测[J].光电工程，2020,47(10):128-137.
[22] 郝博，谷继明，刘力维.基于BF-YOLOv5的红外及可见光图像融合的目标检测[J].电光与控制，2024,31(5):72-76.
[23] REDMON J,DIVVALA S,GIRSHICK R,et al.You Only Look Once:Unified,Real-time Object Detection[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE,2016:779-788.
[24] WENG S M,WANG H,WANG J S,et al.YOLO-SRMX:A Lightweight Model for Real-time Object Detection on Unmanned Aerial Vehicles[J].Remote Sensing,2025,17(13):2313.
[25] NGUYEN P T,NGUYEN G L,BUI D D.LW-UAV-YOLOv10:A Lightweight Model for Small UAV Detection on Infrared Data Based on YOLOv10[J].Geomatica,2025,77(1):100049.
[26] CHEN Z,ZHANG Y Y,XING S H.YOLO-LE:A Lightweight and Efficient UAV Aerial Image Target Detection Model[J].Computers,Materials & Continua,2025,84(1):1787-1803.
[27] HOU T,LENG C C,WANG J X,et al.MFEL-YOLO for Small Object Detection in UAV Aerial Images[J].Expert Systems with Applications,2025,291:128459.
[28] HAN Y Q,WANG C C,LUO H,et al.LRDS-YOLO Enhances Small Object Detection in UAV Aerial Images with a Lightweight and Efficient Design[J].Scientific Reports,2025,15:22627.
[29] LIU W,ANGUELOV D,ERHAN D,et al.SSD:Single Shot MultiBox Detector[C]//Computer Vision-ECCV 2016.Amsterdam:Springer,2016:21-37.
[30] 李国强，孙英家.基于改进SSD的无人机航拍目标检测[J].空间控制技术与应用，2023,49(1):65-73.
[31] 胡焱，原子昊，涂晓光，等.基于对比学习的改进SSD目标检测算法[J].红外技术，2024,46(5):548-555.
[32] 张娜，戚旭磊，包晓安，等.基于优化预测定位的单阶段目标检测算法[J].浙江大学学报(工学版),2022,56(4):783-794.
[33] HE K M,ZHANG X Y,REN S Q,et al.Deep Residual Learning for Image Recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE,2016:770-778.
[34] LIU Z,LIN Y T,CAO Y,et al.Swin Transformer:Hierarchical Vision Transformer Using Shifted Windows[C]// 2021 IEEE/CVF International Conference on Computer Vision (ICCV).Montreal:IEEE,2021:9992-10002.
[35] 肖沁，李正周，刘海毅.基于场景自适应方向引导滤波的红外成像非均匀性校正方法[J].光子学报，2024,53(11):253-265.
[36] 聂丰英，万里勇.等分直方图均衡和高频强化的低光照图像增强[J/OL].计算机科学与探索，2025,1-11[2025-05-20].https://link.cnki.net/urlid/11.5602.tp.20250422.1104.002.
[37] 吕本远，禚真福，韩永赛，等.基于Faster区域卷积神经网络的目标检测[J].激光与光电子学进展，2021,58(22):257-265.
[38] 吕鹏远，兰金江，曾学仁，等.基于特征增强与融合的红外目标检测算法[J].红外技术，2024,46(7):782-790.
[39] 文靖杰，王勇，李金龙，等.多头自注意力机制的Faster R-CNN目标检测算法[J].现代电子技术，2024,47(7):8-16.
[40] 陈金吉，吴金明，许吉慧，等.基于域适应的无人机航拍目标检测算法[J].计算机应用与软件，2025,42(5):298-305.
[41] 杨晨，侯志强，李新月，等.基于CNN-Transformer双模态特征融合的目标检测算法[J].光子学报，2024,53(3):280-293.
[42] 汪西晨，彭富伦，李业勋，等.基于改进Faster R-CNN的红外目标检测算法[J].应用光学，2024,45(2):346-353.
[43] QIAN X L,JIAN Q Q,WANG W,et al.Incorporating Multiscale Context and Task-consistent Focal Loss into Oriented Object Detection[J].IEEE Transactions on Geoscience and Remote Sensing,2025,63:1-11.
[44] 汪常建，丁勇，卢盼成.融合改进FPN与关联网络的Faster R-CNN目标检测[J].计算机工程，2022,48(2):173-179.
[45] 严波，任仰勋，方登洲，等.基于串联Faster R-CNN的绝缘子自爆检测识别[J].计算机技术与发展，2021,31(12):175-179.
[46] CARION N,MASSA F,SYNNAEVE G,et al.End-to-End Object Detection with Transformers[C]//Proceedings of Computer Vision-ECCV 2020.Glasgow:Springer,2020:213-229.
[47] XUE R Z,HUA S J,XU H Q.FECI-RTDETR a Lightweight Unmanned Aerial Vehicle Infrared Small Target Detector Algorithm Based on RT-DETR[J].IEEE Access,2025,13:9578-9591.
[48] LIU J J,XIE Y H.WDFS-DETR:A Transformer-based Framework with Multi-scale Attention for Small Object Detection in UAV Engineering Tasks[J].Results in Engineering,2025,27:105930.
[49] 乔庆元，程换新.基于改进YOLOv8的红外无人机目标检测算法[J].激光与红外，2024,54(12):1941-1947.
[50] 刘坤，李基，晏行伟，等.基于多维特征优化的红外小型无人机目标检测[J/OL].光子学报，2025,1-13[2025-06-29].https://link.cnki.net/urlid/61.1235.04.20250619.1708.012.
[51] 郑展骥，冯昌奎，赵杨洋，等.无人机航拍视角下密集场景非机动车小目标检测方法[J].交通运输系统工程与信息，2025,25(3):1-22.
[52] 张明淳，牛春晖，刘力双，等.用于无人机探测系统的红外小目标检测算法[J].激光技术，2024,48(1):114-120.
[53] FANG H Z,DING L,WANG X L,et al.SCINet:Spatial and Contrast Interactive Super-resolution Assisted Infrared UAV Target Detection[J].IEEE Transactions on Geoscience and Remote Sensing,2024,62:1-22.
[54] 张瑞芳，杜伊婷，程小辉.无人机视角下小目标检测算法[J].激光与光电子学进展，2025,62(4):360-368.
[55] 翟亚红，陈雅玲，徐龙艳，等.改进YOLOv8s的轻量级无人机航拍小目标检测算法[J].浙江大学学报(工学版),2025,59(8):1-10.
[56] 赵兴科，李明磊，张弓，等.基于显著图融合的无人机载热红外图像目标检测方法[J].自动化学报，2021,47(9):2120-2131.
[57] 何奇晗，李中旭，任凌飞，等.细节与全局多级融合的红外无人机目标检测[J].红外技术，2024,47(7):1-8.
[58] WU P,YAO T,HE W M,et al.GA-DETR:A Lightweight and Efficient Infrared Target Detection Method for UAVs[J].The Journal of Supercomputing,2025,81:1-26.
[59] YUAN X Y,XIA J,WU J,et al.Low Altitude Small UAV Detection Based on YOLO Model[C]// 2020 39th Chinese Control Conference (CCC).Shenyang:IEEE,2020:7362-7366.
[60] ZHAO X F,ZHANG W W,ZHANG H,et al.ITD-YOLOv8:An Infrared Target Detection Model Based on YOLOv8 for Unmanned Aerial Vehicles[J].Drones,2024,8(4):161.
[61] 李海源，黄俊.基于YOLOv8的无人机红外航拍目标检测方法[J].激光杂志，2025,46(2):73-81.
[62] ZHAO X F,XIA Y T,ZHANG W W,et al.YOLO-ViT-based Method for Unmanned Aerial Vehicle Infrared Vehicle Target Detection[J].Remote Sensing,2023,15(15):3778.
[63] WANG S,LIU Y Y,WANG X H,et al.An Improved YOLO Algorithm for UAV Detection in Formation Flight[J].Signal,Image and Video Processing,2025,19:1-8.
[64] 张建君，陈玉丹，刘玉玲，等.基于改进YOLOv5s模型的红外弱小目标检测方法[J].应用光学，2024,45(5):975-981.
[65] HE A,LI X B,WU X M,et al.ALSS-YOLO:An Adaptive Lightweight Channel Split and Shuffling Network for TIR Wildlife Detection in UAV Imagery[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2024,17:17308-17326.
[66] WANG S,JIANG H P,LI Z J,et al.PHSI-RTDETR:A Lightweight Infrared Small Target Detection Algorithm Based on UAV Aerial Photography[J].Drones,2024,8(6):240.
[67] WANG C,WANG R D,WU Z W,et al.YOLO-UIR:A Lightweight and Accurate Infrared Object Detection Network Using UAV Platforms[J].Drones,2025,9(7):479.
基本信息:
中图分类号:V279;TP391.41;TN219
引用信息:
[1]徐倩,韩楠,董文安,等.无人机红外目标检测技术：现状与展望[J].无线电工程,2025,55(09):1764-1774.
基金信息:
国家自然科学基金(62272066); 四川省科技计划资助(2025ZNSFSC0044,2025YFHZ0194); 成都重点研发支撑计划产业链协同创新项目(2025-XT00-00005-GX); 成都市技术创新研发项目重点项目(2025-YF08-00016-GX); 成都市区域科技创新合作项目(2025-YF11-00050-HZ); 成都市技术创新研发项目(2024-YF05-01217-SN); 网络空间安全教育部重点实验室及河南省网络空间态势感知重点实验室开放基金课题(KLCS20240106); 网络空间大数据智能安全教育部重点实验室开放基金课题(CBDIS202404)~~
2025-08-15
2025-08-15
2025-08-15