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.