SAR-LtYOLOv8:A Lightweight YOLOv8 Model for Small Object Detection in SAR Ship Images  

作　　者：Conghao Niu Dezhi Han Bing Han Zhongdai Wu 

机构地区：[1]School of Information Engineering,Shanghai Maritime University,Shanghai,201306,China [2]Shanghai Ship and Shipping Research Institute Co.,Ltd.,Shanghai,200135,China

出　　处：《Computer Systems Science & Engineering》2024年第6期1723-1748,共26页计算机系统科学与工程（英文）

基　　金：supported by the Open Research Fund Program of State Key Laboratory of Maritime Technology and Safety in 2024;the National Natural Science Foundation of China(Grant No.52331012);the Natural Science Foundation of Shanghai(Grant No.21ZR1426500).

摘　　要：The high coverage and all-weather capabilities of Synthetic Aperture Radar(SAR)image ship detection make it a widely accepted method for maritime ship positioning and identification.However,SAR ship detection faces challenges such as indistinct ship contours,low resolution,multi-scale features,noise,and complex background interference.This paper proposes a lightweight YOLOv8 model for small object detection in SAR ship images,incorporating key structures to enhance performance.The YOLOv8 backbone is replaced by the Slim Backbone(SB),and the Delete Medium-sized Detection Head(DMDH)structure is eliminated to concentrate on shallow features.Dynamically adjusting the convolution kernel weights of the Omni-Dimensional Dynamic Convolution(ODConv)module can result in a reduction in computation and enhanced accuracy.Adjusting the model’s receptive field is done by the Large Selective Kernel Network(LSKNet)module,which captures shallow features.Additionally,a Multi-scale Spatial-Channel Attention(MSCA)module addresses multi-scale ship feature differences,enhancing feature fusion and local region focus.Experimental results on the HRSID and SSDD datasets demonstrate the model’s effectiveness,with a 67.8%reduction in parameters,a 3.4%improvement in AP(average precision)@0.5,and a 5.4%improvement in AP@0.5:0.95 on the HRSID dataset,and a 0.5%improvement in AP@0.5 and 1.7%in AP@0.5:0.95 on the SSDD dataset,surpassing other state-of-the-art methods.

关 键 词：SAR ship detection MSCA deep learning 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]