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An Improved Algorithm for Facet-based Infrared Small Target Detection

机译：一种基于方面的红外小目标检测改进算法

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摘要

Infrared small target detection is an important research area of computer vision and often a key technique in Infrared Search and Track (IRST) systems. Many algorithms have been reported for this purpose. The facet-based method is one of novel algorithms and is shown as robust and efficient, but it does not perform well in target preservation. The method cannot detect peripheral pixel of target, which causes information loss of target intensity distribution and affects post processing of detection, such as target tracking and recognition. In this paper an improved algorithm is developed for solving this shortcoming. The detection behavior of the facet model is further analyzed. Small target is surrounded by background, so local image edge that indicates target contour can be represented by zero-crossings of the second partial derivatives. The improved algorithm uses facet model to fit local intensity surface and detect potential targets using extremum theory, then the zero-crossings of the second partial derivatives of the fitting function in each potential target’s neighborhood are found and the pixels inside the zero-crossing contour are restored to the potential target. In experiments involving typical infrared images target intensity distribution information is well preserved by proposed algorithm and its execution time is also acceptable.
机译：红外小目标检测是计算机视觉的重要研究领域，并且通常是红外搜索和跟踪（IRST）系统中的一项关键技术。为此已经报道了许多算法。基于分面的方法是一种新颖的算法，被证明具有鲁棒性和高效性，但是在目标保留方面却表现不佳。该方法无法检测到目标的周围像素，从而导致目标强度分布的信息丢失，并影响目标跟踪和识别等检测后处理。在本文中，为解决该缺点，提出了一种改进的算法。对构面模型的检测行为进行了进一步分析。小目标被背景包围，因此指示目标轮廓的局部图像边缘可由二阶导数的零交叉表示。改进的算法使用小平面模型拟合局部强度表面并使用极值理论检测潜在目标，然后在每个潜在目标的邻域中找到拟合函数的二阶导数的零交叉，并且零交叉轮廓内的像素为恢复到潜在目标。在涉及典型红外图像的实验中，所提出的算法可以很好地保留目标强度分布信息，并且其执行时间也是可以接受的。

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