The sparse representation based on over-complete dictionary is a new image representation theory. The redundancy of over-complete dictionary can enable it effectively to capture the geometrical characteristics of the images. In this paper, a novel detection method based on image sparse representation was introduced. The over-complete target dictionary is first constructed with atoms which are produced by two-dimensional Gaussian model. Then the sub-image blocks of the test image are extracted successively and the corresponding coefficients are calculated with the constructed over-complete target dictionary. There is a significant difference between the coefficients of objective and background. Whether the sub-image block contains small target or not can be determined by the index of sparse concentration. Experimental results demonstrated the effectiveness of the proposed method.