针对现有背景抑制算法未能有效抑制背景而导致目标检测率低的问题,提出了一种基于人工免疫网络(aiNet)进行背景抑制、基于行列k均值聚类实现阈值分割的单帧红外弱小目标检测算法。首先采用aiNet结合Robinson警戒环技术,融入自组织特征映射(SOM)拓扑思想,设计一系列抗体进化策略,建立自适应局部空间背景模型—模糊拓扑记忆抗体库,并以此分析各像素点的背景模糊隶属度来抑制背景杂波;接着提出基于行列k均值聚类的阈值分割算法来检测真实目标。实验结果表明,该算法的F1指标高达99%,其能随背景的局部变化来自适应建立空间背景模型,从而自适应抑制背景杂波突显目标,能有效提高信噪比检测弱小目标。

In order to solve the problem that the current approaches cannot suppress the background clutters effectively,which results in a poor detection performance,a new infrared dim target detection approach is presented,which is based on background suppression by artificial immune network (aiNet) and threshold segmentation by k-means cluster of rows and columns. First,the aiNet is combined with Robinson guard to build the adaptive local spatial background models as fuzzy topological memory antibody bank. In the process of antibody bank modeling,a series of antibody evolution strategies are designed based on self-organizing maps (SOM). With these models,background clutters are suppressed according to the degree of fuzzy match between pixels and models. Then,the proposed adaptive segmentation algorithm based on k-means cluster of rows and columns is used to detect the true targets. Experimental results show that the F1 measurement of the proposed approach is up to 99%. The proposed approach is able to build the spatial background models adaptively according to the local change of image,and suppress the background clutters and highlight the targets effectively. It is capable of improving the signal-to-noise ratio of images and detecting targets effectively.