Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

Impact Factor

1.60

CiteScore

Wencheng Wu1 and Askar Hamdulla This email address is being protected from spambots. You need JavaScript enabled to view it.1

1Institute of Information Science and Engineering, Xinjiang University, Urumqi 830046, China


 

Received: May 15, 2019
Accepted: May 10, 2020
Publication Date: September 1, 2020

Download Citation: ||https://doi.org/10.6180/jase.202009_23(3).0014  

ABSTRACT


With the development of infrared monitoring and early warning system, more and more attention has been paid to the research of infrared small target detection. How to effectively detect the target in a complex background has always been a major challenge for researchers. This paper presents a space-time-based detection for small infrared target, which can improve the performance of infrared small target detection system. Firstly, the targets are enhanced and the background clutter is suppressed by the morphological filter. Then, the segmentation images method is proposed to erase the majority noises, which calculates an adaptive thresholds based on the constant false alarm rate (CFAR). Finally, a dynamic detection is proposed to erase all of the noises in the multi-frame, which adjust the size and position of the search window based on the kinematic rule of targets. Experimental results show that this technology can effectively detect the small infrared target with high performance.


Keywords: morphology filter; constant false alarm rate CFAR; motion feature analysis; point target detection; SNR of point target


REFERENCES


 

  1. [1]Wang, B. , Dong, L. , Zhao, M. , & Xu, W. . (2017). Fast infrared maritime target detection: binarization via histogram curve transformation. Infrared Physics & Technology, 83, 32-44.
  2. [2]Hu, J. , Yu, Y. , & Liu, F. . (2015). Small and dim target detection by background modeling. SPIE Optical Engineering + Applications.
  3. [3]Yang, C. , Ma, J. , Qi, S. , Tian, J. , & Tian, X. . (2015). Directional support value of gaussian transformation for infrared small target detection. Applied Optics, 54(9).
  4. [4]Li Y., H. Lu and J. Li. (2016) Underwater image de-scattering and classification by deep neural network[J]. Computers & Electrical Engineering, 54:68-77.
  5. [5]Han, J. , Ma, Y. , Huang, J. , Mei, X. , & Ma, J. . (2016). An infrared small target detecting algorithm based on human visual system. IEEE Geoscience and Remote Sensing Letters, 1-5..
  6. [6]Liu G., F. Wang and Z. Liu. (2016) Infrared aerial small target detection based on digital image processing[J]. Multimedia Tools & Applications, 76(19), 1-15.
  7. [7]Qin, Y. , & Li, B. . (2016). Effective infrared small target detection utilizing a novel local contrast method. IEEE Geoscience and Remote Sensing Letters, 1-5.
  8. [8]Wang, X. , & Chen, C. . (2017). Ship detection for complex background sar images based on a multiscale variance weighted image entropy method. IEEE Geoscience and Remote Sensing Letters, 14(2), 184-187.
  9. [9]Venkateswarlu R. (1999) Max-mean and max-median filters for detection of small targets[J]. Proceedings of SPIE - The International Society for Optical Engineering, 3809, 74-83.
  10. [10]Nie, J. , Qu, S. , Wei, Y. , Zhang, L. , & Deng, L. . (2018). An infrared small target detection method based on multiscale local homogeneity measure. Infrared Physics & Technology, S1350449517305078.
  11. [11]Deng, L. , Zhu, H. , Zhou, Q. , & Li, Y. . (2018). Adaptive top-hat filter based on quantum genetic algorithm for infrared small target detection. Multimedia Tools and Applications, 77(9), 10539-10551.
  12. [12]Bai X. and F. Zhou. (2010) Analysis of new top-hat transformation and the application for infrared dim small target detection[M]. Elsevier Science Inc.
  13. [13]Zhang, H. , Niu, Y. , & Zhang, H. . (2017). Small target detection based on difference accumulation and gaussian curvature under complex conditions. Infrared Physics & Technology, S1350449517300476.
  14. [14]Han, J. , Liang, K. , Zhou, B. , Zhu, X. , Zhao, J. , & Zhao, L. . (2018). Infrared small target detection utilizing the multiscale relative local contrast measure. IEEE Geoscience & Remote Sensing Letters, 1-5.
  15. [15]Yang X, Zhou Y, Zhou D, et al. (2015). A new infrared small and dim target detection algorithm based on multi-directional composite window. Infrared Physics & Technology, 71, 402-407.
  16. [16]Qian, K. , Zhou, H. , Rong, S. , Wang, B. , & Cheng, K. . (2017). Infrared dim-small target tracking via singular value decomposition and improved kernelized correlation filter. Infrared Physics & Technology, 82, 18-27.
  17. [17]Sun, X. , Liu, X. , Tang, Z. , Long, G. , & Yu, Q. . (2017). Real-time visual enhancement for infrared small dim targets in video. Infrared Physics & Technology, 83, 217-226..
  18. [18]Wan, M. , Gu, G. , Cao, E. , Hu, X. , Qian, W. , & Ren, K. . (2016). In-frame and inter-frame information based infrared moving small target detection under complex cloud backgrounds. Infrared Physics & Technology, 76, 455-467.
  19. [19]Nasiri, M. , & Chehresa, S. . (2017). Infrared small target enhancement based on variance difference. Infrared Physics & Technology, 82, 107-119.
  20. [20]Yu K., Z. Shi and X. Lu. (2017) Infrared small target detection method based on local threshold attenuation of constant false alarm[C]// Society of Photo-Optical Instrumentation Engineers. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series.
  21. [21]Wang X., C. Wang and Y. Zhang. (2010) Research on SNR of Point Target Image. Electronics Optics & Control, 17(1), 18-21. (In Chinese).
  22. [22]Han, K. Liang, B. Zhou, X. Zhu, J. Zhao, L. Zhao, (2018)"Infrared small target detection utilizing the multiscale relative local contrast measure", IEEE Geosci. Remote Sens. Lett., vol. 15, no. 4, pp. 612-616, Apr. 2018.