Ms. Kejia Yi Profile

Kejia Yi

at Harbin Engineering Univ

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Proceedings Article | 8 December 2011 Paper

An improved algorithm for facet-based infrared small target detection

Kejia Yi, Tingquan Deng, Jing Guan, Gongze Wang, Hao Chen

Proceedings Volume 8003, 80030V (2011) https://doi.org/10.1117/12.901966

KEYWORDS: Target detection, Detection and tracking algorithms, Infrared radiation, Infrared detectors, Target recognition, Infrared imaging, Algorithm development, Evolutionary algorithms, Infrared search and track, Algorithms

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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.

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