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一种基于深层次多尺度特征融合CNN的SAR图像舰船目标检测算法

SAR Ship Detection Based on Convolutional Neural Network with Deep Multiscale Feature Fusion

杨龙   苏娟   黄华   李响  
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摘要

基于深度学习的目标检测技术在目标检测领域有强大的生命力,但是将其用于合成孔径雷达(SAR)图像舰船目标检测时并没有达到预期的效果。提出了一种基于卷积神经网络的SAR图像舰船目标检测算法用来检测多场景下的多尺度舰船目标,在单发多盒探测器检测框架的基础上,使用性能更好的Darknet-53作为特征提取网络,加入更深层次的特征融合网络,生成语义信息更加丰富的新的特征预测图。同时在训练策略上使用了一种新的二分类损失函数来解决训练过程中难易样本失衡的问题。在扩展的公开SAR图像舰船数据集上进行验证实验,实验结果表明,所提方法对复杂场景下不同尺寸的舰船目标的检测展现出了良好的适应性。

Abstract

Objectdetection technology based on deep learning has shown excellent performance in the field of object detection; however, it has not yielded expected results when used for synthetic aperture radar (SAR) ship detection. Herein, an SAR ship detection method based on a convolutional neural network is proposed for multiscale ship detection in multiple scenarios. Based on the single shot multiBox detector, we use Darknet-53 as the feature extraction network. A deep feature fusion network is added to generate new feature prediction maps with rich semantic information. In addition, we use a new two-class loss function in the training strategy to deal with the imbalance in the difficult and easy samples in the training process. The verification experiments are performed on the expanded public SAR ship detection dataset. The experimental results indicate that our proposed method has a good adaptability to SAR ship detection at different sizes in complex scenes.

Newport宣传-MKS新实验室计划
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中图分类号:TP751.1

DOI:10.3788/AOS202040.0215002

所属栏目:机器视觉

基金项目:国家自然科学基金;

收稿日期:2019-07-30

修改稿日期:2019-09-09

网络出版日期:2020-02-01

作者单位    点击查看

杨龙:火箭军工程大学核工程学院, 陕西 西安 710025
苏娟:火箭军工程大学核工程学院, 陕西 西安 710025
黄华:西北工业大学航海学院, 陕西 西安 710072
李响:火箭军工程大学核工程学院, 陕西 西安 710025

联系人作者:苏娟(yangl03@mail.nwpu.edu.cn)

备注:国家自然科学基金;

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引用该论文

Yang Long,Su Juan,Huang Hua,Li Xiang. SAR Ship Detection Based on Convolutional Neural Network with Deep Multiscale Feature Fusion[J]. Acta Optica Sinica, 2020, 40(2): 0215002

杨龙,苏娟,黄华,李响. 一种基于深层次多尺度特征融合CNN的SAR图像舰船目标检测算法[J]. 光学学报, 2020, 40(2): 0215002

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