[1]王言鹏,杨飏,姚远.用于内河船舶目标检测的单次多框检测器算法[J].哈尔滨工程大学学报,2019,40(07):1258-1262.[doi:10.11990/jheu.201805057]
 WANG Yanpeng,YANG Yang,YAO Yuan.Single shot multibox detector for ships detection in inland waterway[J].hebgcdxxb,2019,40(07):1258-1262.[doi:10.11990/jheu.201805057]
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用于内河船舶目标检测的单次多框检测器算法(/HTML)
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《哈尔滨工程大学学报》[ISSN:1006-6977/CN:61-1281/TN]

卷:
40
期数:
2019年07期
页码:
1258-1262
栏目:
出版日期:
2019-07-05

文章信息/Info

Title:
Single shot multibox detector for ships detection in inland waterway
作者:
王言鹏 杨飏 姚远
大连理工大学 船舶工程学院, 辽宁 大连 110624
Author(s):
WANG Yanpeng YANG Yang YAO Yuan
School of Naval Architecture, Dalian University of Technology, Dalian 116024, China
关键词:
目标检测背景建模内河卷积神经网络单次多框检测器样本增强
分类号:
TP391.4
DOI:
10.11990/jheu.201805057
文献标志码:
A
摘要:
针对传统目标检测算法在内河水运环境受外界条件影响过大的问题,本文提出了基于单次多框检测器的内河船舶目标检测方法。单次多框检测器模型基于卷积神经网络,使用全图各个位置的多尺度区域特征进行回归,使图像可以直接作为网络的输入,避免了由于波浪、树叶晃动等外界因素产生的误检。同时,对于内河船舶样本不足的问题,应用样本增强和迁徙学习的方法训练船舶目标检测的网络模型,有效缓解了训练过程中的过拟合现象,取得了较好的检测效果。经内河不同地区的多组船舶视频检测表明:此方法具有更好的鲁棒性和更低的误检率,船舶的识别率均超过了90%,比传统的背景建模算法提高16%以上。

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2018-05-14。
基金项目:国家自然科学基金项目(51261120376).
作者简介:王言鹏,男,硕士研究生;杨飏,女,副教授.
通讯作者:杨飏,E-mail:yyang@dlut.edu.cn
更新日期/Last Update: 2019-07-04