[1]王德文,宋学帅,李成浩,等.基于边缘增强和多尺度特征融合的遥感图像船舰检测[J].智能系统学报,2026,21(1):60-71.[doi:10.11992/tis.202505014]
WANG Dewen,SONG Xueshuai,LI Chenghao,et al.Ship detection in remote sensing images using edge enhancement and multi-scale feature fusion[J].CAAI Transactions on Intelligent Systems,2026,21(1):60-71.[doi:10.11992/tis.202505014]
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WANG Dewen,SONG Xueshuai,LI Chenghao,et al.Ship detection in remote sensing images using edge enhancement and multi-scale feature fusion[J].CAAI Transactions on Intelligent Systems,2026,21(1):60-71.[doi:10.11992/tis.202505014]
基于边缘增强和多尺度特征融合的遥感图像船舰检测
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
21
期数:
2026年第1期
页码:
60-71
栏目:
学术论文—机器学习
出版日期:
2026-03-05
- Title:
- Ship detection in remote sensing images using edge enhancement and multi-scale feature fusion
- Keywords:
- remote sensing image; ship detection; high-frequency feature; edge enhancement; multi-scale; feature fusion; lightweight detection head; YOLO
- 分类号:
- TP751
- 摘要:
- 遥感图像中的船舰目标具有尺度变化剧烈、分布密集和方向朝向任意的特点，特别是船舰与海洋环境之间对比度低，相邻船舰之间边界模糊，这使船舰检测面临更高的挑战。针对以上问题，本文提出了一种基于边缘增强和多尺度特征融合的遥感图像船舰检测模型。设计了高频特征增强模块，提升模型捕获细节的能力；提出了一种边缘信息引导的多尺度特征融合方法，缓解浅层边缘信息在传递过程中丢失的问题；构建轻量化定向检测头，减少模型参数量。实验结果表明，改进后的模型在ShipRSImageNet数据集和HRSC2016数据集上，平均检测精度(mAP50)较YOLO11-obb模型分别提升3.6和2.1百分点，有效提升遥感图像船舰检测的精度。
- Abstract:
- Ship objects in remote sensing images exhibit large scale variation, dense distribution, and arbitrary orientation. In particular, the low contrast between ships and the ocean background, along with blurred boundaries between adjacent ships, poses greater challenges for detection. To address these issues, a model based on edge enhancement and multi-scale feature fusion for ship detection in remote sensing images was proposed. Firstly, a high-frequency feature enhancement module was designed to improve the ability of the model to capture fine details. Furthermore, an edge-guided multi-scale feature fusion method was proposed to mitigate the loss of edge information on low-level during propagation. Finally, a lightweight oriented detection head was constructed to reduce the params of the model Experimental results show that the improved model improves 3.6 and 2.1 percentage points of mAP50 on the ShipRSImageNet dataset and the HRSC2016 Dataset, compared to the YOLO11-obb model, effectively improves the accuracy of ship detection in remote sensing images.
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备注/Memo
收稿日期:2025-5-21。
基金项目:国家自然科学基金项目(62371188).
作者简介:王德文，副教授，主要研究方向为人工智能与图像处理。主持或参与国家自然科学基金项目4项；获省科技进步奖3项；以第一完成人获国家专利授权3项；发表学术论文50余篇。E-mail：wde@ncepu.edu.cn。;宋学帅，硕士研究生，主要研究方向为人工智能与遥感图像处理。E-mail：ncepuxs@163.com。;李成浩，硕士研究生，主要研究方向为人工智能与图像处理。E-mail：patricklee@163.com。
通讯作者:王德文. E-mail：wde@ncepu.edu.cn
更新日期/Last Update:
2026-01-05