基于神经网络的扇形扫描图像微小人造物体检测

S. Perry, L. Guan
{"title":"基于神经网络的扇形扫描图像微小人造物体检测","authors":"S. Perry, L. Guan","doi":"10.1109/OCEANS.2001.968325","DOIUrl":null,"url":null,"abstract":"This paper presents a neural network based system to detect small man-made objects in sequences of sector scan sonar images. The detection of such objects is considered out to ranges of 150 metres using a forward-looking sonar system mounted on a vessel. After an initial cleaning operation performed by compensating for the motion of the vessel, the imagery was segmented to extract objects for analysis. A set of 31 features extracted from each object was examined. These features consisted of basic object size and contrast features, shape moment-based features, moment invariants, and features extracted from the second-order histogram of each object. The best set of 15 features was then selected using sequential forward selection and sequential backward selection. These features were then used to train a neural network to detect man-made objects in the image sequences. The detector achieved a 97% accuracy at a mean false positive rate of 9 per frame.","PeriodicalId":326183,"journal":{"name":"MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings (IEEE Cat. No.01CH37295)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Detection of small man-made objects in sector scan imagery using neural networks\",\"authors\":\"S. Perry, L. Guan\",\"doi\":\"10.1109/OCEANS.2001.968325\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a neural network based system to detect small man-made objects in sequences of sector scan sonar images. The detection of such objects is considered out to ranges of 150 metres using a forward-looking sonar system mounted on a vessel. After an initial cleaning operation performed by compensating for the motion of the vessel, the imagery was segmented to extract objects for analysis. A set of 31 features extracted from each object was examined. These features consisted of basic object size and contrast features, shape moment-based features, moment invariants, and features extracted from the second-order histogram of each object. The best set of 15 features was then selected using sequential forward selection and sequential backward selection. These features were then used to train a neural network to detect man-made objects in the image sequences. The detector achieved a 97% accuracy at a mean false positive rate of 9 per frame.\",\"PeriodicalId\":326183,\"journal\":{\"name\":\"MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings (IEEE Cat. No.01CH37295)\",\"volume\":\"86 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings (IEEE Cat. No.01CH37295)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/OCEANS.2001.968325\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings (IEEE Cat. No.01CH37295)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/OCEANS.2001.968325","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11

摘要

提出了一种基于神经网络的扇形扫描声纳图像序列小物体检测系统。使用安装在船上的前视声纳系统,可以在150米范围内探测到此类物体。通过补偿血管的运动进行初始清洗操作后,图像被分割以提取对象进行分析。从每个目标中提取31个特征进行检查。这些特征包括基本的目标大小和对比度特征、基于形状矩的特征、矩不变量和从每个目标的二阶直方图中提取的特征。然后使用顺序正向选择和顺序向后选择来选择15个特征的最佳集合。然后,这些特征被用来训练神经网络来检测图像序列中的人造物体。该检测器在每帧平均误报率为9的情况下达到了97%的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection of small man-made objects in sector scan imagery using neural networks
This paper presents a neural network based system to detect small man-made objects in sequences of sector scan sonar images. The detection of such objects is considered out to ranges of 150 metres using a forward-looking sonar system mounted on a vessel. After an initial cleaning operation performed by compensating for the motion of the vessel, the imagery was segmented to extract objects for analysis. A set of 31 features extracted from each object was examined. These features consisted of basic object size and contrast features, shape moment-based features, moment invariants, and features extracted from the second-order histogram of each object. The best set of 15 features was then selected using sequential forward selection and sequential backward selection. These features were then used to train a neural network to detect man-made objects in the image sequences. The detector achieved a 97% accuracy at a mean false positive rate of 9 per frame.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信