A speeded-up saliency region-based contrast detection method for small targets

Zhengjie Li; Haiying Zhang; Jiaojiao Bai; Zhongjun Zhou; Huihuang Zheng

doi:10.1117/12.2302488

10 April 2018 A speeded-up saliency region-based contrast detection method for small targets

Zhengjie Li, Haiying Zhang, Jiaojiao Bai, Zhongjun Zhou, Huihuang Zheng

Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 1061506 (2018) https://doi.org/10.1117/12.2302488
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

Abstract

To cope with the rapid development of the real applications for infrared small targets, the researchers have tried their best to pursue more robust detection methods. At present, the contrast measure-based method has become a promising research branch. Following the framework, in this paper, a speeded-up contrast measure scheme is proposed based on the saliency detection and density clustering. First, the saliency region is segmented by saliency detection method, and then, the Multi-scale contrast calculation is carried out on it instead of traversing the whole image. Second, the target with a certain “integrity” property in spatial is exploited to distinguish the target from the isolated noises by density clustering. Finally, the targets are detected by a self-adaptation threshold. Compared with time-consuming MPCM (Multiscale Patch Contrast Map), the time cost of the speeded-up version is within a few seconds. Additional, due to the use of “clustering segmentation”, the false alarm caused by heavy noises can be restrained to a lower level. The experiments show that our method has a satisfied FASR (False alarm suppression ratio) and real-time performance compared with the state-of-art algorithms no matter in cloudy sky or sea-sky background.

Citation Download Citation

Zhengjie Li, Haiying Zhang, Jiaojiao Bai, Zhongjun Zhou, and Huihuang Zheng "A speeded-up saliency region-based contrast detection method for small targets", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061506 (10 April 2018); https://doi.org/10.1117/12.2302488

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