Infrared dim-small target detection based on an improved multiscale fractal feature

Yu GU; Jun LIU; Hong-hai SHEN; Dong-liang PENG; Ying XU

doi:10.3788/OPE.20202806.1375

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Infrared dim-small target detection based on an improved multiscale fractal feature

更新时间：2020-07-13

- Infrared dim-small target detection based on an improved multiscale fractal feature
- Optics and Precision Engineering Vol. 28, Issue 6, Pages: 1375-1386(2020)
- 作者机构：
  
  1.杭州电子科技大学自动化学院，浙江杭州 310018
  2.中国科学院长春光学精密机械与物理研究所，吉林长春 130033
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20202806.1375
  CLC： TP753
- Received：30 December 2019，
  
  Accepted：10 February 2020，
  
  Published：15 June 2020
- 稿件说明：
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Yu GU, Jun LIU, Hong-hai SHEN, et al. Infrared dim-small target detection based on an improved multiscale fractal feature[J]. Optics and precision engineering, 2020, 28(6): 1375-1386.
DOI：

Yu GU, Jun LIU, Hong-hai SHEN, et al. Infrared dim-small target detection based on an improved multiscale fractal feature[J]. Optics and precision engineering, 2020, 28(6): 1375-1386. DOI： 10.3788/OPE.20202806.1375.

摘要

为提高红外图像弱小目标检测的准确率和实时性，在分析用于红外图像增强的分形参数

相关的多尺度分形特征(MFFK)基础上，提出了一种基于改进多尺度分形特征(IMFFK)的红外图像弱小目标检测算法。首先，将基于地毯覆盖法的分形维数计算公式代入MFFK计算公式，提出了一种改进多尺度分形特征(IMFFK)用于图像增强。其次，对IMFFK特征计算进行简化，采用自适应阈值分割得到感兴趣目标区域，提出了一种具有较高计算效率的红外图像弱小目标检测算法。最后，通过仿真图像分析了主要参数对图像增强和算法耗时的影响，采用红外真实图像进行了算法检测性能测试，并与当前基于局部对比度测度的目标检测算法进行了对比。实验结果表明，提出的算法虽然在一些检测场景具有较多虚警，但能同时适用于弱小目标和较大目标检测，且无论目标为亮目标或暗目标。提出算法对于低分辨率红外图像(320×240)检测接近30 frame/s。提出算法具有较强的适用性，能够检测出红外图像中具有较高局部对比度的目标。

Abstract

To improve the accuracy and real-time performance of infrared (IR) dimsmall target detection

an IR dimsmall object detection algorithm based on an improved multi-scale fractal feature was presented.Computational analysis of the multi-scale fractal feature related to the fractal parameter K (MFFK)

which was used for IR image enhancement in the algorithm

was performed. First

an improved multi-scalefractal feature (IMFFK) was presented to perform image enhancement after substituting the equation for computing fractal dimension into the equation for computing MFFK using the covering-blanket method. Thereafter

a computationally efficient IR dimsmall target detection algorithm was presented

in which the computation of IMFFK was simplified and an adaptive threshold was used to segment targets of interest from the background. Finally

the effect of primary parameters on image enhancement and computational cost was analyzed based on the simulation images. The IR real-world images were subsequently used to evaluate the detection performance of the proposed algorithm

and comparisons with state-of-the-art detection algorithms based on local contrast measureare performed. The proposed algorithm was capable of simultaneously detecting dimsmall and large targets in an IR image

irrespective of whether the targets were bright or dark

even though false alarms were detected in some scenarios. It is also capable of reachingapproximately 30 frames per second for low-resolution IR images (320×240). The proposed algorithm exhibitssatisfactory applicability and can be used to detect targets with high local contrast in an image.

关键词

Keywords

references

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