Infrared Small Target Detection by Density Peaks Searching and Maximum-Gray Region Growing

Robust detection of infrared small target is still a challenge due to the diversity and complexity of the background. In this letter, we propose a novel detection approach based on density peaks searching and maximum-gray region growing. The main idea is that infrared small targets can be described by three features: a relatively high density, a relatively large distance from pixels with higher density, and a relatively large density gap between targets and their neighbors. This idea helps to establish a detection procedure which can detect small targets of different sizes and remove the interference caused by clutters of various complex shapes. A quartile-based technique is introduced to obtain a more robust decision threshold for multiple scenes. Compared with eight state-of-the-art algorithms, the proposed method shows a superior detection performance and an acceptable efficiency in extensive experiments.