红外主动成像和识别 -

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红外主动成像和识别

doi:10.3788/CO.20130605.0795

1.
海军驻长春地区航空军事代表室, 吉林长春 130033;
2.
中国科学院长春光学精密机械与物理研究所与物质相互作用国家重点实验室, 吉林长春 130033

基金项目:

吉林省重大科技攻关专项资金资助项目(No.20120615)

详细信息

作者简介:
赵建川(1969-),男,吉林长春人,学士,工程师,1992年于海军电子工程学院获得学士学位,主要从事光电设备研制与改造方面的研究。E-mail:zaojc@ciomp.ac.cn

通讯作者:
郭劲

中图分类号:TN249;TP391
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- 文章访问数:2975
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- 被引次数:0
出版历程
- 收稿日期:2013-07-15
- 修回日期:2013-09-13
- 刊出日期:2013-10-10

Infrared laser active imaging and recognition technology

1.
Aviation Commissary of Navy in Changchun, Changchun 130033, China;
2.
State Key Laboratory of Laser Interaction with Matter, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China

摘要

摘要:在传统主动成像系统的基础上,结合目标识别技术搭建了一台主动成像识别系统实验平台,研究了主动成像后的目标识别技术。由7个不变Hu矩构成特征量,用由136个权值系数构成BP神经网络算法对黑夜条件下450 m处的运动目标43式冲锋模具枪进行了实验研究。研究显示,采用该方法成功获得了清晰的红外主动成像效果,对2 740 frame 主动成像图像的统计目标识别率达到了68.87%,其中旋转变换下的统计识别率可达80.05%。该项研究对实际黑夜暗小目标的探测识别具有重要意义。
- 主动成像/
- 红外成像/
- 目标识别/
- 成像距离模型/
- Hu矩特征量/
- BP神经网络
Abstract:An experiment platform for laser active imaging and target recognition was built combining a laser active image system and the target recognition technology, and the target recognition after laser active imaging was mainly researched. The feature vector was comprised of seven invariant Hu moments. The BP neural network algorithm comprised of 136 weight coefficients was used to study the moving target, a 43 submachine gun model at 450 m from the experiment platform at night, and excellent experiment results were obtained. It shows clear imaging effects by 68.87% of target recognition statistic probability in 2 740 frames of laser active imaging, and the probability of rotation transformation reaches 80.05%. These researches are significant to the detection and recognition of little targets at night.
- laser active imaging/
- infrared imaging/
- target recognition/
- imaging distance model/
- Hu moment feature vector/
- BP neural network

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参考文献 (1)

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