摘要
介绍了一个用于水下机器人的光视觉目标识别系统,描述了该系统的硬件结构和软件体系.论述了水下图像的处理方法,分析了在离散状态下比例因子对不变矩特征的影响,构造了基于不变矩的仿射变换不变量.详细描述了用于目标识别的免疫遗传神经网络的结构和建模.通过对4类实测水下目标的识别实验,证月了所提水下目标识别系统可以用于水下目标识别,并且具有较高的准确性和实时性.
A target recognition system based on optical vision for AUV （autonomous underwater vehicle） is presented, and the hardware structure and software system of the vision system are described. Underwater image processing techniques are introduced, and considering the influence of scale factors on moment invariant features in discrete conditions, affine transformation invariants based on invariant moments are constructured. Structure and modeling of the immune genetic neural network （IGNN） applied to target recognition are discussed in detail. Experiments of underwater target recognition are made on four types of actual targets, and the results prove that the presented underwater target recognition system can be applied to underwater image recognition with relatively high real-time performance and accuracy.
出处
《机器人》
EI
CSCD
北大核心
2009年第2期171-178,共8页
Robot
基金
国家863计划资助项目(2008AA092301)
哈尔滨工程大学基础研究基金资助项目(HEUFT08001)
水下智能机器人技术国防科技重点实验室开放课题研究基金资助项目(2007001).
关键词
自主式水下机器人(AUV)
光视觉
特征提取
免疫遗传神经网络
autonomous underwater vehicle （AUV）
optical vision
feature extraction
immune genetic neural network （IGNN）
