空天预警研究学报2026，Vol.40Issue(2)：105-109,136,6.DOI:10.3969/j.issn.2097-180X.2026.02.006
运用LPE-YOLO算法的海面遥感图像多尺度目标检测方法
Multi-scale target detection method for sea-surface remote sensing images using LPE-YOLO algorithm
陈可仁 1朱旭芳 1赵傅彦1
作者信息
- 1. 海军工程大学电子工程学院,武汉 430030
- 折叠
摘要
Abstract
In order to address the problems such as low detection accuracy and insufficient feature extraction caused by ship targets'significant size-scale differences,large aspect ratios,arbitrary directional arrangements,as well as strong background noise interference in sea-surface remote sensing images,an improved multi-scale target detection algorithm(LPE-YOLO)based on YOLOv8(You Only Look Once version 8)algorithm is proposed.Alin-ear deformable convolution(LDConv)module is introduced into the backbone network to enhance the algorithm's fea-ture fitting ability for geometrically deformed targets.AC2f-PEMAmodule is designed and integrated into the feature fusion network,where partial convolution(PConv)module is utilized to reduce computational redundancy and an effi-cient multi-scale attention(EMA)mechanism is embedded to suppress sea-surface clutter interference and enhance the perception capability of multi-scale targets.Simulation results show that,for the mixed dataset of HRSC2016 and DOTA sea-surface remote sensing images,the LPE-YOLO algorithm achieves a target detection accuracy of 91.8%,which is an improvement compared to baseline algorithm,YOLOv5n,and YOLO11n.关键词
海面遥感图像/目标检测/YOLOv8算法/LDConv模块/C2f-PEMA模块/部分卷积模块/EMA机制Key words
sea-surface remote sensing images/object detection/YOLOv8 algorithm/LDConv module/C2f-PE-MA module/PConv module/EMA mechanism分类
信息技术与安全科学引用本文复制引用
陈可仁,朱旭芳,赵傅彦..运用LPE-YOLO算法的海面遥感图像多尺度目标检测方法[J].空天预警研究学报,2026,40(2):105-109,136,6.
