| 基于局部均值的红外小目标检测算法 |
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| 引用本文： | 刘运龙,薛雨丽,袁素真,毛峡. 基于局部均值的红外小目标检测算法[J]. 红外与激光工程, 2013, 42(3): 814-822 |
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| 作者姓名： | 刘运龙 薛雨丽 袁素真 毛峡 |
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| 作者单位： | 1.北京航空航天大学 电子信息工程学院,北京100191 |
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| 基金项目： | 中央高校基本科研业务费专项资金（YWF-12-LZGF-054） |
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| 摘 要： | 复杂背景条件下红外小目标检测是红外自动寻的、红外预警系统的关键技术和研究热点之一。为了能有效地检测出小目标，对红外图像中的小目标与背景特性进行了分析，在充分利用小目标与其局部背景差异的基础上，提出一种基于局部灰度均值确定红外小目标尺寸和位置信息的算法。首先，给出判断像元属于小目标的必要条件，该条件判定图像中哪些像元可能属于红外小目标；其次，基于可能属于小目标的像元给出小目标可能的尺寸值；再次，对所得结果进行优化，排除虚警；最后，根据前三阶段所得结果确定小目标的尺寸和位置。Matlab 仿真结果表明，对复杂云层背景的红外图像，Top-Hat 检测算法虽然检测速度快，但当虚警和目标的灰度值相等时不能很好地对目标进行检测；新算法在选择合适参数的基础上能准确给出目标的位置信息，并能较好地估算小目标尺寸，然而新算法在检测速度上仍有待进一步提高。
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| 关 键 词： | 局部均值 复杂背景 红外小目标 小目标检测 |
| 收稿时间： | 2012-07-12 |
Infrared small targets detection using local mean |
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| Affiliation: | 1.Institute of Electronic and Information Engineering,Beihang University,Beijing 100191,China |
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| Abstract: | The detection of infrared small targets under the condition of complex background is one of the most important technologies and the most popular research topics in infrared auto target search and infrared guarding systems. In order to detect the small target efficiently, the local gray mean was proposed to determine the size and location of the infrared small target based on the analysis of the characteristics of the small target and its local background. Firstly, the necessary condition to judge whether a pixel was a small target was given. Secondly, based on the pixels which was the target, the possible sizes of the small target could be figured out. Then, the result was optimized and false alarm was eliminated as far as possible. Finally, according to the result obtained from the first three steps, the ultimated size and location of the infrared small target were given. Matlab simulation results show that, for the complex cloud backgrounds, the detection algorithm based on Top-Hat transform has good detection speed, but when the gray values of false alarm and target are equal, the algorithm could not detect the target efficiently. When choosing suitable parameters, the new algorithm not only can accurately give the location information of the small target, but also has good ability to estimate the small target size. However, the detection speed of the new algorithm remains to be further improved. |
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