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A cascade method for infrared dim target detection
Infrared Physics & Technology ( IF 3.3 ) Pub Date : 2021-06-08 , DOI: 10.1016/j.infrared.2021.103768
Jie Li , Pengbo Yang , Wennan Cui , Tao Zhang

Infrared dim target detection acts a pivotal role in searching and tracking applications. Due to the long-range distance, detection tasks are usually confronted with a low signal-to-noise ratio (SNR). To tackle the problem, a cascade method is proposed in this paper. Different from traditional gradient detection, the proposed method takes advantage of movement continuity to settle the pseudo targets elimination. Concisely, there are only three steps contained in cascade method. Initially, since some bad points might exist in the original infrared images, a local order-statistic filter is adopted to dispel pixel-sized fixed noise (PSFN). Secondly, processed infrared images are filtered by a 5 × 5 facet kernel to extract candidate targets from background. Finally, inspired by the movement continuity, a motion continuous pipeline (MCP) is built to achieve pseudo objects elimination. To measure the performance of the proposed method, a set of real infrared images (the SNR is around 1) covering different backgrounds are discussed in experiments. The results indicate that the proposed method outperforms conventional baseline methods in terms of detection rate and false-alarm.



中文翻译:

一种红外暗淡目标检测的级联方法

红外暗淡目标检测在搜索和跟踪应用中起着举足轻重的作用。由于距离远,检测任务通常面临低信噪比(SNR)。为了解决这个问题,本文提出了一种级联方法。与传统的梯度检测不同,该方法利用运动连续性来解决伪目标消除问题。简而言之,级联方法只包含三个步骤。最初,由于原始红外图像中可能存在一些坏点,因此采用局部顺序统计滤波器来消除像素大小的固定噪声(PSFN)。其次,处理后的红外图像由 5×5 小面核过滤以从背景中提取候选目标。最后,受到运动连续性的启发,建立运动连续管道(MCP)以实现伪对象消除。为了测量所提出方法的性能,在实验中讨论了一组覆盖不同背景的真实红外图像(SNR 约为 1)。结果表明,所提出的方法在检测率和误报方面优于传统的基线方法。

更新日期:2021-07-04
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