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A Speeded-up Saliency Region-based Contrast Detection Method for Small Targets

机译：一种基于显着性区域加速的小目标对比度检测方法

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摘要

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.
机译：为了应对红外小目标的实际应用的快速发展，研究人员已竭尽全力寻求更强大的检测方法。目前，基于对比度测量的方法已成为一个有前途的研究分支。根据该框架，本文提出了一种基于显着性检测和密度聚类的加速对比度测量方案。首先，通过显着性检测方法对显着性区域进行分割，然后对它进行多尺度对比度计算，而不是遍历整个图像。其次，利用在空间上具有一定“完整性”特性的目标，通过密度聚类将目标与孤立噪声区分开。最后，通过自适应阈值检测目标。与费时的MPCM（多尺度面片对比度图）相比，加速版本的时间成本在几秒钟之内。另外，由于使用“聚类分割”，可以将由重噪声引起的错误警报抑制在较低水平。实验表明，无论是在多云的天空还是海天的背景下，与最新算法相比，我们的方法都具有令人满意的FASR（虚假警报抑制率）和实时性能。

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