SAR image near-shore ship target detection method in complex background

计算机科学 人工智能 特征提取 特征(语言学) 模式识别(心理学) 卷积(计算机科学) 恒虚警率 计算机视觉 合成孔径雷达 目标检测 假警报 遥感 人工神经网络 地质学 语言学 哲学
作者
Yonggang Li,Weigang Zhu,Chenxuan Li,Chuangzhan Zeng
出处
期刊:International Journal of Remote Sensing [Taylor & Francis]
卷期号:44 (3): 924-952 被引量:10
标识
DOI:10.1080/01431161.2023.2173030
摘要

Due to background clutter in synthetic aperture radar (SAR) images, the detection of dense ship targets suffers from a low detection rate, high false alarm rate, and high missed detection rate. To address this issue, an FSM-DFF-YOLOv5+Confluence algorithm is proposed in this paper for the detection of near-shore ship targets in SAR images with complex backgrounds. First, based on the YOLOv5 target detection algorithm, two improvements are made in the feature extraction network: feature refinement and multi-feature fusion; in the feature extraction network, deformable convolutional neural networks are adopted to change the position of the target sampling points of the convolution to improve the feature extraction capability of the target and the detection rate of ship targets in SAR images with a complex background; in the multi-feature fusion network structure, cascading and parallel pyramids are used in the multi-feature fusion network to realize feature fusion at different levels; the visual perceptual field of feature extraction is expanded by using null convolution to enhance the adaptability of the network to detect near-shore multi-scale ship targets with complex backgrounds and reduce the false alarm rate of ship target detection in SAR images with complex environments. In this way, the DFF-YOLOv5 near-shore ship target detection algorithm is established. Meanwhile, to address the problem of missed detection in near-shore dense ship target detection, this paper adds rectangular convolution kernels to the convolution of the feature extraction network to better realize the feature extraction of dense ship targets in SAR images with complex backgrounds. Besides, the Confluence algorithm instead of non-maximum suppression is used in the prediction stage. Through experiments on the constructed complex background near-shore ship detection dataset, it is indicated that the average accuracy of the FSM-DFF-YOLOv5+Confluence detection algorithm reaches 88.96%, and the recall rate reaches 88.80%.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
思源应助shelly采纳,获得10
1秒前
半城微凉完成签到,获得积分10
1秒前
Ao发布了新的文献求助10
1秒前
2秒前
大美女完成签到,获得积分10
2秒前
lewis_xl完成签到,获得积分10
2秒前
joyland完成签到,获得积分10
2秒前
devin578632完成签到,获得积分10
2秒前
cdh1994应助康康星采纳,获得20
3秒前
Fox发布了新的文献求助10
3秒前
河畔发布了新的文献求助10
4秒前
吗喽完成签到,获得积分10
4秒前
4秒前
Yyan完成签到,获得积分10
4秒前
所所应助炙热的便当采纳,获得10
4秒前
5秒前
就这样发布了新的文献求助10
6秒前
西原的橙果完成签到,获得积分10
6秒前
6秒前
6秒前
深情安青应助疯狂的兰采纳,获得10
7秒前
7秒前
7秒前
跳跃的翼发布了新的文献求助30
8秒前
Lucien给伊斯坦布尔的鱼的求助进行了留言
8秒前
在下天池宫人间行走完成签到,获得积分10
9秒前
不想学习发布了新的文献求助10
9秒前
冬青完成签到 ,获得积分10
9秒前
含糊的鱼发布了新的文献求助10
9秒前
9秒前
Ansen发布了新的文献求助10
9秒前
科研通AI2S应助fu采纳,获得10
9秒前
澈哩发布了新的文献求助10
11秒前
dddhzzz发布了新的文献求助30
11秒前
Fox完成签到,获得积分20
11秒前
纸船完成签到,获得积分10
11秒前
12秒前
爆米花应助张志远采纳,获得10
12秒前
hyy完成签到,获得积分10
12秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 360
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3970683
求助须知:如何正确求助?哪些是违规求助? 3515337
关于积分的说明 11178055
捐赠科研通 3250580
什么是DOI,文献DOI怎么找? 1795357
邀请新用户注册赠送积分活动 875790
科研通“疑难数据库(出版商)”最低求助积分说明 805166