The classical target detection methods may only perform well with certain assumptions. To overcome the shortcomings, this paper proposes a novel local decision adaptive information-theoretic metric learning (LA-ITML) target detector. Firstly, the proposed method uses the ITML method as the objective function for learning a Mahalanobis distance to separate similar and dissimilar point-pairs. Then, a locally decision adaptive constraint is applied to shrink the distances between samples of similar pairs and expand the distances between samples of dissimilar pairs. Finally, we can make the detection decision by considering both the threshold and the changes between the distances before and after metric learning. The experimental results demonstrate that the proposed method can obviously separate target samples from background ones and out performs both the state-of-the-art target detection algorithms and the other classical metric learning methods.