Research on SAR Image Lightweight Detection Based on Improved YOLOV8

作     者：WANG Qing SI Zhan-jun 王庆;司占军

作者机构：School of Institute of Artificial IntelligenceTianjin University of Science and TechnologyTianjin 300457China 

出 版 物：《印刷与数字媒体技术研究》 (Printing and Digital Media Technology Study)

年 卷 期：2025年第1期

页      码：93-100页

摘      要：In recent years,with the development of synthetic aperture radar(SAR)technology and the widespread application of deep learning,lightweight detection of SAR images has emerged as a research *** ultimate goal is to reduce computational and storage requirements while ensuring detection accuracy and reliability,making it an ideal choice for achieving rapid response and efficient *** this regard,a lightweight SAR ship target detection algorithm based on YOLOv8 was proposed in this ***,the C2f-Sc module was designed by fusing the C2f in the backbone network with the ScConv to reduce spatial redundancy and channel redundancy between features in convolutional neural *** the same time,the Ghost module was introduced into the neck network to effectively reduce model parameters and computational complexity.A relatively lightweight EMA attention mechanism was added to the neck network to promote the effective fusion of features at different *** results showed that the Parameters and GFLOPs of the improved model are reduced by 8.5%and 7.0%when mAP@0.5 and mAP@0.5:0.95 are increased by 0.7%and 1.8%,*** makes the model lightweight and improves the detection accuracy,which has certain application value.

主 题 词：YOLOv8 Synthetic aperture radar image Lightweight Target detection 

学科分类：081203[081203] 08[工学] 0835[0835] 0812[工学-测绘类] 

D　O　I：10.19370/j.cnki.cn10-1886/ts.2025.01.010

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