首页|基于特征复用机制的航拍图像小目标检测算法

基于特征复用机制的航拍图像小目标检测算法

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  • NETL
  • NSTL
  • 万方数据
  • 维普
针对无人机(UAV)航拍图像检测存在的小目标检测精度低和模型参数量大的问题，提出轻量高效的航拍图像检测算法FS-YOLO。该算法以YOLOv8s为基准网络，通过降低通道维数和改进网络架构提出轻量的特征提取网络，实现对冗余特征信息的高效复用，在较少的参数量下产生更多特征图，提高模型对特征信息的提取和表达能力，同时显著减小模型大小。在特征融合阶段引入内容感知特征重组模块，加强对小目标显著语义信息的关注，提升网络对航拍图像的检测性能。使用无人机航拍数据集VisDrone进行实验验证，结果表明，所提算法以仅5。48 M的参数量实现了mAP0。5=47。0%的检测精度，比基准算法YOLOv8s的参数量降低了 50。7%，精度提升了6。1%。在DIOR数据集上的实验表明，FS-YOLO的泛化能力较强，较其他先进算法更具竞争力。
Small target detection algorithm for aerial images based on feature reuse mechanism
A lightweight and efficient aerial image detection algorithm called Functional ShuffleNet YOLO(FS-YOLO)was proposed based on YOLOv8s,in order to address the issues of low detection accuracy for small targets and a large number of model parameters in current unmanned aerial vehicle(UAV)aerial image detection.A lightweight feature extraction network was introduced by reducing channel dimensions and improving the network architecture.This facilitated the efficient reuse of redundant feature information,generating more feature maps with fewer parameters,enhancing the model's ability to extract and express feature information while significantly reducing the model size.Additionally,a content-aware feature recombination module was introduced during the feature fusion stage to enhance the attention on salient semantic information of small targets,thereby improving the detection performance of the network for aerial images.Experimental validation was conducted using the VisDrone dataset,and the results indicated that the proposed algorithm achieved a detection accuracy of 47.0%mAP0.5 with only 5.48 million parameters.This represented a 50.7%reduction in parameter count compared to the YOLOv8s benchmark algorithm,along with a 6.1%improvement in accuracy.Experimental results of DIOR dataset showed that FS-YOLO had strong generalization and was more competitive than other state-of-the-art algorithms.

unmanned aerial vehicle(UAV)imageobject detectionYOLOv8lightweight backboneCARAFE

邓天民、程鑫鑫、刘金凤、张曦月

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重庆交通大学交通运输学院,重庆 400074

无人机(UVA)图像 目标检测 YOLOv8 轻量化主干 CARAFE

国家重点研发计划资助项目重庆市技术创新与应用发展专项重点资助项目重庆市技术创新与应用发展专项重点资助项目重庆市技术创新与应用发展专项重点资助项目

2022YFC3800502cstc2021jscxgksbX0058CSTB2022TIAD-KPX0113CSTB2022TIAD-KPX0118

2024

10.3785/j.issn.1008-973X.2024.03.001

浙江大学学报(工学版)
浙江大学

浙江大学学报(工学版)

CSTPCD北大核心
影响因子：0.625
ISSN：1008-973X
年,卷(期)：2024.58(3)
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