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Infrared small target detection technology based on OpenCV

机译:基于OpenCV的红外小目标检测技术

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Accurate and fast detection of infrared (IR) dim target has very important meaning for infrared precise guidance, early warning, video surveillance, etc. In this paper, some basic principles and the implementing flow charts of a series of algorithms for target detection are described. These algorithms are traditional two-frame difference method, improved three-frame difference method, background estimate and frame difference fusion method, and building background with neighborhood mean method. On the foundation of above works, an infrared target detection software platform which is developed by OpenCV and MFC is introduced. Three kinds of tracking algorithms are integrated in this software. In order to explain the software clearly, the framework and the function are described in this paper. At last, the experiments are performed for some real-life IR images. The whole algorithm implementing processes and results are analyzed, and those algorithms for detection targets are evaluated from the two aspects of subjective and objective. The results prove that the proposed method has satisfying detection effectiveness and robustness. Meanwhile, it has high detection efficiency and can be used for real-time detection.
机译:准确,快速地检测出红外弱点目标对于红外精确制导,预警,视频监控等具有十分重要的意义。本文介绍了一系列目标检测算法的一些基本原理和实现流程图。 。这些算法是传统的两帧差分方法,改进的三帧差分方法,背景估计和帧差分融合方法以及用邻域均值法构建背景。在以上工作的基础上,介绍了由OpenCV和MFC开发的红外目标检测软件平台。该软件集成了三种跟踪算法。为了清楚地说明软件,本文描述了其框架和功能。最后,对一些真实的红外图像进行了实验。对整个算法的实现过程和结果进行了分析,从主观和客观两个方面对检测目标的算法进行了评价。结果证明,该方法具有令人满意的检测效果和鲁棒性。同时,具有较高的检测效率,可用于实时检测。

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