Abstract: Detecting small target correctly in infrared images is an important topic in the field of image processing. Existing methods do not fully explore the characteristics of small targets, therefore, targets are easily submerged in the complex background. Furthermore, target trajectory information can not be fully utilized, making it difficult to distinguish the target from the isolated noise. To solve above problems, a novel infrared small target detection method was proposed based on multi-attribute morphology and improved pipeline filter. Firstly, to achieve target enhancement and background suppression, a max-tree was constructed for infrared image, and some features, such as area, height and diagonal attributes, were extracted synthetically from small target. Then, fusing the results of multi-attribute morphology, the candidate targets were determined. And utilizing an improved pipeline filter, considering the regularity of target motion, the target-like noise was removed from the candidate targets. Finally, some experiments were carried out to compare the proposed method with other four methods on four image data sets. The experiment results show that the proposed method can suppress the background clutter to the maximum extent and enhance the target simultaneously. Furthermore, the proposed method can keep its robust to the targets with different types, diverse sizes, and large brightness differences in a variety of complex scenes.