在复杂背景及强噪声干扰的场景中，红外小目标因其尺寸小、信号弱、缺乏文理特征等特点，极易湮没在背景和噪声中，导致检测虚警率高、算法复杂、计算量大等问题。为此，本文提出一种基于频域残差及局部协方差的红外弱小目标检测方法。首先，通过频域残差计算红外图像的显著图，以获得目标可能存在的区域。然后，在此区域内利用局部协方差检测方法做识别。最后，通过自适应阈值分割得到真实目标。对不同复杂背景的红外图像进行小目标检测实验，结果表明，与传统检测算法相比，该算法在不同场景下都能有效抑制背景和噪声，准确检测目标，且满足实时性要求。
In complex scenes, the infrared small target can be lost in the background and noise easily, due to features of small size, weak signal, lack of texture, resulting in high false alarm detection rate, complex algorithm, large amount of calculation and other problems. We present an infrared dim small target detection method based on spectral residuals and local covariance. Firstly, the saliency map can be obtained by calculating the spectral residuals of the original infrared images, which can obtain the possible region of the target. Secondly, the local covariance detection method can be used for identification in this area. Finally, the small target can be detected by adaptive threshold segmentation method.Experimental results indicate that compared with traditional detection algorithms, the proposed algorithm can effectively suppress background and noise in different scenes, accurately detect targets, and meet the real-time requirements.
2023,45(23): 139-144 收稿日期：2022-12-02
DOI：10.3404/j.issn.1672-7649.2023.23.024
分类号：TP391.4
作者简介：李栋(1988-),男,硕士,高级工程师,从事目标识别、综合指控研究
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