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基于深渊鱼类识别的原位自主观测方法
Alternative TitleIn-situ autonomous observation method based on hadal fish recognition
陈俊1,2; 张奇峰1; 张艾群1; 蔡笃思
Department水下机器人研究室
Source Publication吉林大学学报(工学版)
ISSN1671-5497
2019
Volume49Issue:3Pages:953-962
Contribution Rank1
Funding Organization中国科学院战略性先导专项(B 类)(XDB06040100)
Keyword海洋工程与技术 自主观测 支持向量机 鱼类识别 深渊生物 摄像系统
Abstract为提高深渊鱼类观测效率,针对传统预编程式观测方法无法感知目标的不足,提出了一种基于鱼类识别的自主观测方法。首先,通过改进的背景差分法快速分割运动目标;其次,结合深渊生物特点提出了基于Fisher判别函数的形状特征提取方法,然后使用粒子群算法(PSO)优化的支持向量机(SVM)分类法实现了鱼类的识别。最后,设计了深渊鱼类的自主观测算法,并提出了一种观测效率的评价方法。使用深渊原位观测视频进行模拟观测实验的结果表明本文算法可有效提高观测效率。
Other AbstractIn-situ observation of hadal fish has been widely implemented for scientific research, usually on serial time or fixed time interval observation mode. However, the observation efficiency is extremely low since pre-programmed method cannot perceive the interested targets in camera view. A novel autonomous observation method combined with computer vision technology is proposed in this paper, where observation strategy could be dynamically adjusted according to the result of fish recognition. Moving targets are rapidly segmented from video frames based on improved background difference method. Invariant moment, eccentricity and roundness characteristics are extracted subsequently, and Fisher discriminant function is used for feature reduction. Fish target prediction model is then established with SPO-SVM algorism. The effectiveness of proposed autonomous observation method is validated through simulation experiment using in-situ observation video data of hadal trench expedition.
Language中文
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/22087
Collection水下机器人研究室
Corresponding Author张奇峰
Affiliation1.中国科学院沈阳自动化研究所机器人学国家重点实验室;
2.中国科学院大学
Recommended Citation
GB/T 7714
陈俊,张奇峰,张艾群,等. 基于深渊鱼类识别的原位自主观测方法[J]. 吉林大学学报(工学版),2019,49(3):953-962.
APA 陈俊,张奇峰,张艾群,&蔡笃思.(2019).基于深渊鱼类识别的原位自主观测方法.吉林大学学报(工学版),49(3),953-962.
MLA 陈俊,et al."基于深渊鱼类识别的原位自主观测方法".吉林大学学报(工学版) 49.3(2019):953-962.
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