A fast-saliency method for real-time infrared small target detection

Abstract

Infrared small target detection plays an important role in applications including military reconnaissance, early warning and terminal guidance. In this paper, we present a fast method, called fast-saliency, with very low computational complexity, for real-time small target detection in single image frame under various complex backgrounds. Different from traditional algorithms, the proposed method is inspired by a recent research on visual saliency detection indicating that small salient signals could be well detected by a gradient enhancement operation combined with Gaussian smoothing, which is able to delineate regions of small targets in infrared images. Concisely, there are only four simple steps contained in fast-saliency. In order, they are gradient operation, square computation, Gaussian smoothing and automatic thresholding, representing the four procedures as highpass filtering, target enhancement, noise suppression and target segmentation, respectively. Especially, for the most crucial step, gradient operation, we innovatively propose a 5×5 facet kernel operator that holds the key for separating the small targets from backgrounds. To verify the effectiveness of our proposed method, a set of real infrared images covering typical backgrounds with sea, sky and ground clutters are tested in experiments. The results demonstrate that it outperforms the state-of-the-art methods not only in detection accuracy, but also in computation efficiency.