遥感影像船舶检测的特征金字塔网络建模方法

P237; 船舶作为海上运输载体,其准确检测在海洋环境保护、海上渔业生产管理、海上交通与应急处置及国防安全应用中都具有重要意义和价值.目前基于目标检测网络的遥感船舶检测方法因末层特征分辨率不足和卷积固定的几何结构,导致网络难以适应小尺度且具有随机朝向、形态多变特征的船舶目标,进而限制船舶检测精度.针对该问题,本文提出一种用于遥感影像船舶检测的特征金字塔网络建模方法.首先引入形变卷积/RoI池化模块,以适应朝向和形态多变的船舶目标;其次借鉴在小目标检测中性能出色的特征金字塔网络的建模思想,采用对称式网络和多尺度特征融合的方式进一步融合高级语义和低级空间信息,提升小尺度目标特征分辨率.在40000...

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Published in测绘学报 Vol. 49; no. 6; pp. 787 - 797
Main Authors 邓睿哲, 陈启浩, 陈奇, 刘修国
Format Journal Article
LanguageChinese
Published 中国地质大学(武汉)地理与信息工程学院,湖北武汉,430074 01.06.2020
Subjects
船舶检测
特征金字塔网络
形变卷积模块
形变RoI池化模块
Online AccessGet full text
ISSN1001-1595
DOI10.11947/j.AGCS.2020.20190117

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Abstract P237; 船舶作为海上运输载体,其准确检测在海洋环境保护、海上渔业生产管理、海上交通与应急处置及国防安全应用中都具有重要意义和价值.目前基于目标检测网络的遥感船舶检测方法因末层特征分辨率不足和卷积固定的几何结构,导致网络难以适应小尺度且具有随机朝向、形态多变特征的船舶目标,进而限制船舶检测精度.针对该问题,本文提出一种用于遥感影像船舶检测的特征金字塔网络建模方法.首先引入形变卷积/RoI池化模块,以适应朝向和形态多变的船舶目标;其次借鉴在小目标检测中性能出色的特征金字塔网络的建模思想,采用对称式网络和多尺度特征融合的方式进一步融合高级语义和低级空间信息,提升小尺度目标特征分辨率.在40000幅、船舶目标67280余个的遥感影像数据集上的试验结果表明,本文方法能够有效集成形变卷积/RoI池化和多尺度特征融合方法,相较传统CNN船舶检测方法取得明显提升,在准确率、召回率及F1指标上分别达到85.8％、97.9％ 和91.5％.
AbstractList P237; 船舶作为海上运输载体,其准确检测在海洋环境保护、海上渔业生产管理、海上交通与应急处置及国防安全应用中都具有重要意义和价值.目前基于目标检测网络的遥感船舶检测方法因末层特征分辨率不足和卷积固定的几何结构,导致网络难以适应小尺度且具有随机朝向、形态多变特征的船舶目标,进而限制船舶检测精度.针对该问题,本文提出一种用于遥感影像船舶检测的特征金字塔网络建模方法.首先引入形变卷积/RoI池化模块,以适应朝向和形态多变的船舶目标;其次借鉴在小目标检测中性能出色的特征金字塔网络的建模思想,采用对称式网络和多尺度特征融合的方式进一步融合高级语义和低级空间信息,提升小尺度目标特征分辨率.在40000幅、船舶目标67280余个的遥感影像数据集上的试验结果表明,本文方法能够有效集成形变卷积/RoI池化和多尺度特征融合方法,相较传统CNN船舶检测方法取得明显提升,在准确率、召回率及F1指标上分别达到85.8％、97.9％ 和91.5％.
Author 刘修国
邓睿哲
陈启浩
陈奇
AuthorAffiliation 中国地质大学(武汉)地理与信息工程学院,湖北武汉,430074
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DENG Ruizhe
CHEN Qi
CHEN Qihao
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Keywords 船舶检测
特征金字塔网络
形变卷积模块
形变RoI池化模块
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Snippet P237; 船舶作为海上运输载体,其准确检测在海洋环境保护、海上渔业生产管理、海上交通与应急处置及国防安全应用中都具有重要意义和价值.目前基于目标检测网络的遥感船舶检测...
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Title 遥感影像船舶检测的特征金字塔网络建模方法
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