计算机科学

计算机科学 ›› 2026, Vol. 53 ›› Issue (8): 201-208.doi: 10.11896/jsjkx.250800100

• 计算机图形学 & 多媒体 • 上一篇    下一篇

改进YOLOv11n的无人机航拍图像小目标检测模型

王井阳, 薛为民, 黄敏, 武少广   

  1. 河北科技大学信息科学与工程学院 石家庄 050018
  • 收稿日期:2025-08-25 修回日期:2025-10-29 出版日期:2026-08-15 发布日期:2026-08-17
  • 通讯作者: 武少广(wushaoguang@hebust.edu.cn)
  • 作者简介:(ever211@163.com)
  • 基金资助:
    国防科技重点实验室基金项目(6142205240201)

Improved YOLOv11n Model for Small Target Detection in UAV Aerial Images

WANG Jingyang, XUE Weimin, HUANG Min, WU Shaoguang   

  1. School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
  • Received:2025-08-25 Revised:2025-10-29 Published:2026-08-15 Online:2026-08-17
  • About author:WANG Jingyang,born in 1971,professor,master’s supervisor,is a member of CCF(No.51341S).His main research interests include artificial intelligence,computer vision and deep learning.
    WU Shaoguang,born in 1987,master,lecturer.His main research interests include deep learning and artificial intelligence.
  • Supported by:
    Key Laboratories for National Defense Science and Technology(6142205240201).

PDF (PC)

134

摘要： 随着目标检测算法和无人机技术的持续革新,无人机航拍图像中小目标的检测已成为研究热点。无人机航拍图像具有小目标占比高、分布密集和环境复杂等问题,对此,提出了一种改进YOLOv11n的无人机航拍图像小目标检测模型MPI-YOLO。首先,在其Backbone中使用多尺度特征聚合模块(MSFAM)替换前2个C3k2模块,以增强对小目标信息的特征提取能力。其次,针对无人机图像中小目标占比高的问题,新增了P2小目标检测层,使得尺寸微小的目标能够被更好地检测到。最后,提出了改进的双向密集特征金字塔网络(IBDFPN),通过扩展金字塔网络的层级范围并引入跳跃连接来增强浅层和深层特征的跨层交互,实现了高效的多尺度信息融合。在VisDrone2019数据集上,开展了消融实验和对比实验。消融实验验证了各改进策略的有效性,对比实验结果表明,MPI-YOLO对小目标检测的准确率优于其他对比模型,相较于基准模型,改进的YOLOv11n在mAP50上提升了9.1个百分点。在UAVDT数据集和Tinyperson数据集上进行了泛化实验,验证了其具有良好的泛化能力。

关键词: 小目标检测, 无人机航拍图像, 特征金字塔网络, 多尺度特征融合, 双向特征融合

Abstract: With the continuous innovation of target detection algorithms and UAV(Unmanned Aerial Vehicle) technology,detecting small targets in UAV aerial images has become a hot research topic.UAV aerial images have problems such as a high proportion of small targets,dense distribution,and complex environments.To address these issues,this paper proposes an improved UAV aerial image small target detection model MPI-YOLO based on YOLOv11n.Firstly,the MSFAM(Multi-Scale Feature Aggregation Module) replaces the first two C3k2 modules in the Backbone of YOLOv11n to enhance the feature extraction ability for small target information.Secondly,to solve the problem of a high proportion of small targets in UAV images,a new P2 small target detection layer is added,so that small targets can be better detected.Finally,IBDFPN(Improved Bidirectional Dense Feature Pyramid Network) is proposed,which extends the level range of the pyramid network and introduces skip connections to enhance the cross-layer interactions between shallow and deep features,achieving efficient multi-scale information fusion.The VisDrone2019 dataset is utilized for ablation experiments and comparative experiments.The ablation experiments verify the effectiveness of each improved strategy.The comparative experimental results show that MPI-YOLO outperforms other comparison mo-dels in small target detection accuracy.Compared with the benchmark model YOLOv11n,the mAP50 of MPI-YOLO increases by 9.1 percentage points.Generalization experiments are conducted on the UAVDT dataset and Tinyperson dataset,verifying its good generalization ability.

Key words: Small target detection, UAV aerial images, Feature pyramid network, Multi-scale feature fusion, Bidirectional feature fusion

中图分类号: 

  • TP391
[1] ZHANG L,WANG M,FU Y,et al.A forest fire recognition method using UAV images based on transfer learning[J].Fo-rests,2022,13(7):975.
[2] JIANG C,REN H,YE X,et al.Object detection from UAVthermal infrared images and videos using YOLO models[J].International Journal of Applied Earth Observation and Geoinformation,2022,112:102912.
[3] BAO W,REN Y,WANG N,et al.Detection of abnormal vibration dampers on transmission lines in UAV remote sensing images with PMA-YOLO[J].Remote Sensing,2021,13(20):4134.
[4] CHEN C,ZHENG Z,XU T,et al.YOLO-based UAV technology:A review of the research and its applications[J].Drones,2023,7(3):190.
[5] WU X,LI W,HONG D,et al.Deep learning for Unmanned Ae-rial Vehicle-based object detection and tracking:A survey[J].IEEE Geoscience and Remote Sensing Magazine,2022,10(1):91-124.
[6] BOUGUETTAYA A,ZARZOUR H,KECHIDA A,et al.Vehicle detection from UAV imagery with deep learning:A review[J].IEEE Transactions on Neural Networks and Learning Systems,2022,33(11):6047-6067.
[7] CHENG H Y,WENG C C,CHEN Y Y.Vehicle detection in aerial surveillance using dynamic Bayesian networks[J].IEEE Transactions on Image Processing,2012,21(4):2152-2159.
[8] MORANDUZZO T,MELGANI F,BAZI Y,et al.A fast object detector based on high-order gradients and Gaussian process regression for UAV images[J].International Journal of Remote Sensing,2015,36(10):2713-2733.
[9] REN S,HE K,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.
[10] HE K,GKIOXARI G,DOLLAR P,et al.Mask R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision(ICCV).New York:IEEE,2017:2980-2988.
[11] CAI Z,VASCONCELOS N.Cascade R-CNN:Delving into high quality object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).New York:IEEE,2018:6154-6162.
[12] LIU W,ANGUELOV D,ERHAN D,et al.SSD:Single shot multibox detector[C]//Lecture Notes in Computer Science.Berlin:Springer,2016:21-37.
[13] REDMON J,FARHADI A.YOLOv3:An incremental improvement[J].arXiv:1804.02767,2018.
[14] LIN T Y,GOYAL P,GIRSHICK R,et al.Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision(ICCV).New York:IEEE,2017:2980-2988.
[15] ZHU X,SU W,LU L.Deformable DETR:Deformable transformers for end-to-end object detection[J].arXiv:2010.04159,2020.
[16] MU A,WANG H,MENG W,et al.Small target detection in drone aerial images based on feature fusion[J].Signal,Image and Video Processing,2024,18(1):585-598.
[17] ZHU X,LYU S,WANG X,et al.TPH-YOLOv5:ImprovedYOLOv5 based on transformer prediction head for object detection on drone-captured scenarios[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops(ICCV Workshops).New York:IEEE,2021:2778-2788.
[18] ZHOU L,LIU Z,ZHAO H,et al.A multi-scale object detector based on coordinate and global information aggregation for UAV aerial images[J].Remote Sensing,2023,15(10):2567-2580.
[19] PENG S,FAN X,TIAN S.PS-YOLO:A small object detector based on efficient convolution and multi-scale feature fusion[J].Multimedia Systems,2024,30(5):241.
[20] ZHAO Z J,WANG Y,TANG R Q,et al.Based on improved YOLOv11 for UAV target detection algorithm[J].Journal of Computer Applications,2025,45(S2):329-334.
[21] ZHOU W N,WU Z H,ZHANG Z D,et al.Lightweight small target detection method based on weak feature enhancement[J].Control and Decision,2024,39(2):381-390.
[22] SZEGEDY C,LIU W,JIA Y,et al.Going deeper with convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).New York:IEEE,2015:1-9.
[23] JIANG L,YUAN B,WANG Y,et al.MA-YOLO:A method for detecting surface defects of aluminum profiles with attention guidance[J].IEEE Access,2023,11:71269-71286.
[24] LIS,TONG Q,LIU X,et al.MA2-FPN for tiny object detection from remote sensing images[C]//Proceedings of the 15th International Congress on Image Signal Processing,Biomedicine Engineering and Informatics(CISP-BMEI).Piscataway,NJ:IEEE,2022:1-8.
[25] JIANG L,YUAN B,DU J,et al.MFFSODNet:Multiscale feature fusion small object detection network for UAV aerial images[J].IEEE Transactions on Instrumentation and Measurement,2024,73:5015214.
[26] DU D,ZHU P,WEN L,et al.VisDrone-DET2019:The visionmeets drone object detection in image challenge results[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops.Piscataway,NJ:IEEE,2019:213-226.
[27] XU S,ZHANG M,Chen J,et al.YOLO-HyperVision:A vision transformer backbone-based enhancement of YOLOv5 for detection of dynamic traffic information[J].Egyptian Informatics Journal,2024,27:100523.
[28] YU H,LI G,ZHANG W,et al.The unmanned aerial vehicle benchmark:object detection,tracking and baseline[C]//Proceedings of the European Conference on Computer Vision(ECCV).Berlin:Springer,2018:370-386.
[29] YU X,GONG Y,JIANG N,et al.Scale match for tiny person detection[C]//IEEE Winter Conference on Applications of Computer Vision.Snowmass Village,2020:1257-1265.
[30] DENG S,LI S,XIE K,et al.A global-local self-adaptive network for drone-view object detection[J].IEEE Transactions on Image Processing,2021,30:1556-1569.
[31] ZHANG Y,WU C,GUO W,et al.CFANet:efficient detection of UAV image based on cross-layer feature aggregation[J].IEEE Transactions on Geoscience and Remote Sensing,2023,61:5608911.
[32] ZHAO Q,LIU B,LYU S,et al.TPH-YOLOv5++:boostingobject detection on drone-captured scenarios with cross-layer asymmetric transformer[J].Remote Sensing,2023,15(6):1687.
[33] DUAN C,WEI Z,ZHANG C,et al.Coarse-grained density map guided object detection in aerial images[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops.Piscataway,NJ:IEEE,2021:2789-2798.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
地址：重庆市两江新区洪湖西路18号    邮编：401121  
电话：023-63500828（编辑部） 023-67039612（会议/专题） 023-67039623（财务/发票）
E-mail：jsjkx12@163.com
本系统由北京玛格泰克科技发展有限公司设计开发