摘要
合成孔径雷达(SAR)图像船舶目标检测在民用和军事领域发挥着越来越重要的作用。然而,SAR图像中的船舶具有密集排列、任意方向和多尺度等特点。针对这些问题,文章提出了一种改进YOLOv11的SAR图像定向船舶检测方法YOLOv11-FM。首先,设计了一种快速混合聚合网络FMANet,增强网络的特征学习和提取能力。其次,提出了一种双向自适应特征融合网络BAFFN,通过跨层连接的方式实现更丰富的特征交互与融合。最后,采用小波特征增强模块WFU,改进颈部网络中上采样融合模块,增强船舶的细节信息。实验结果表明,YOLOv11-FM在RSDD-SAR船舶目标检测数据集上的P和AP50分别达到了94%和97.6%,具有良好的检测效果。
Synthetic Aperture Radar(SAR)imagery for ship target detection plays an increasingly important role in civil and military applications.However,ships in SAR images are characterized by dense ar-rangement,arbitrary orientation,and multi-scale.To address these issues,this paper proposes a directional ship detection method,YOLOv11-FM,for SAR images with improved YOLOv11.Firstly,a fast hybrid aggregation network,FMANet,is designed to enhance the feature learning and extrac-tion capability of the network.Secondly,a bidirectional adaptive feature fusion network BAFFN is proposed to realize richer feature interaction and fusion through cross-layer connection.Finally,the wavelet feature enhancement module WFU is used to improve the up-sampling fusion module in the neck network and enhance the ship’s detailed information.The experimental results show that YOLOv11-FM has a good P and AP50 of 94%and 97.6%,respectively,on the RSDD-SAR ship tar-get detection dataset.
作者
李亚森
Yasen Li(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai)
出处
《建模与仿真》
2025年第5期789-797,共9页
Modeling and Simulation
