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In-frame and inter-frame information based infrared moving small target detection under complex cloud backgrounds

机译:复杂云背景下基于帧内和帧间信息的红外移动小目标检测

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Infrared moving small target detection under complex cloud backgrounds is one of the key techniques of infrared search and track (IRST) systems. This paper proposes a novel method based on in-frame inter frame information to detect infrared moving small targets accurately. For a single frame, in the spatial domain, a directional max-median filter is developed to make a pre-processing and a background suppression filtering template is utilized on the denoised image to highlight target. Then, targets in cloud regions and non-cloud regions are extracted by different thresholds according to a cloud discrimination method so that a spatial domain map (SDM) is acquired. In the frequency domain, we design an o:-DoB band-pass filter to conduct coarse saliency detection and make an amplitude transformation with smoothing processing which is the so-called elaborate saliency detection. Furthermore, a frequency domain map (FDM) is acquired by an adaptive binary segmentation method. Lastly, candidate targets in single frame are extracted by a discrimination based on intensity and spatial distance criteria. For consecutive frames, a false alarm suppression is conducted on account of differences of motion features between moving target and false alarms to improve detection accuracy again. Large numbers of experiments demonstrate that the proposed method has satisfying detection effectiveness and robustness for infrared moving small target detection under complex cloud backgrounds. (C) 2016 Elsevier B.V. All rights reserved.
机译:复杂云背景下的红外移动小目标检测是红外搜索与跟踪(IRST)系统的关键技术之一。本文提出了一种基于帧内帧间信息的新方法,可以准确地检测红外移动小目标。对于单个帧,在空间域中,开发了定向最大中值滤波器以进行预处理,并在去噪图像上使用背景抑制滤波模板来突出显示目标。然后,根据云判别方法通过不同的阈值提取云区域和非云区域中的目标,从而获取空间域图(SDM)。在频域中,我们设计了一个o:-DoB带通滤波器以进行粗略的显着性检测,并通过平滑处理进行幅度转换,这就是所谓的精细显着性检测。此外,通过自适应二进制分段方法获取频域图(FDM)。最后,通过基于强度和空间距离标准的鉴别来提取单个帧中的候选目标。对于连续的帧,由于移动目标和虚假警报之间运动特征的差异而进行虚假警报抑制,以再次提高检测精度。大量实验表明,该方法对复杂云背景下的红外移动小目标检测具有令人满意的检测效果和鲁棒性。 (C)2016 Elsevier B.V.保留所有权利。

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