摘要： In order to detect dim and small infrared targets from a mass of high-resolution images of omni-directional Infrared Search and Track (IRST) systems rapidly and accurately, a fast target detection method guided by visual saliency (TDGS) is proposed. In this method, a coarse-to-fine detection strategy is used. First, in the stage of coarse-detection, according to the differences of global features between targets and backgrounds, a global saliency model based on fast spectral scale space (FSSS) is constructed to suppress complex background regions rapidly. And visual salient regions which contain dim and small targets are extracted from the original image. Then, in the stage of fine-detection, according to differences of local contrast between targets and background, an adaptive local contrast method (ALCM) is applied to finely improve contrast of targets in visual salient regions. Candidate targets can be further extracted through the adaptive threshold segmentation. Finally, dim and small targets are detected by their temporal relativity in multi-frames. Experimental results on four typical image sequences have indicated that the proposed method can not only detect dim and small infrared targets with small amount of computation, high detection probability, and low false alarm rate, but also adapt to various complex backgrounds. It is suitable for dim and small targets detection in omni-directional IRST systems and other practical applications.