Abstract: In order to solve the problem that the current approaches cannot suppress the background clutters effectively and may result in a poor detection performance,a novel infrared dim target detection approach is presented,which is based on background suppression by fuzzy adaptive resonance theory(Fuzzy-ART) and threshold segmentation by k-means cluster of rows and columns.Firstly,infrared dim target training set is simulated according to the principle of thermal imagery.Then a Fuzzy-ART neural network is utilized to build the target models.With these models,the background clutters are suppressed according to the degree of fuzzy match between pixels and models.Lastly,the adaptive segmentation algorithm based on k-means cluster of rows and columns is utilized to detect the true targets.Experimental results show that the proposed approach is able to suppress background clutters and enhance objects effectively.It is capable of improving the signal-to-noise ratio of images and detecting targets effectively.