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Volume 44 Issue 7
Jul.  2022
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LIN Suzhen, ZHANG Haisong, LU Xiaofei, LI Dawei, LI Yi. RBNSM: a New Method for Infrared Dim and Small Target Detection in Complex Backgrounds[J]. Infrared Technology , 2022, 44(7): 667-675.
Citation: LIN Suzhen, ZHANG Haisong, LU Xiaofei, LI Dawei, LI Yi. RBNSM: a New Method for Infrared Dim and Small Target Detection in Complex Backgrounds[J]. Infrared Technology , 2022, 44(7): 667-675.
shu

RBNSM: a New Method for Infrared Dim and Small Target Detection in Complex Backgrounds

  • 1.

    College of Big Data, North University of China, Taiyuan 030051, China

  • 2.

    Jiuquan Satellite Launch Center, Jiuquan 735305, China

  • 3.

    College of Electrical and Control Engineering, North University of China, Taiyuan 030051, China

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  • Received Date: October 09, 2021
  • Revised Date: December 07, 2021
    Graphical Abstract
    • Abstract
  • Infrared dim and small target (IRDST) detection is a longstanding and challenging problem in infrared search and track systems. To address the problems of a low detection rate and high false alarm rate for dim and small targets in complex backgrounds, a method is proposed for detecting IRDSTs using a regional bi-neighborhood saliency map (RBNSM). First, using the local a-priori property of the weak target, a sliding window is defined and divided into multiple cells before the mean value of the first maximum gray levels of the central cell is calculated to highlight the weak target. Then, the adjacent and spaced neighbors of the central cell are constructed and the mean value of their respective gray levels is calculated. Subsequently, the salient maps of the two neighbors are the extracted from different directions and multiplied point by point to further suppress the clutter background and enhance the weak target. Finally, the target is accurately detected by adaptive extraction. The detection results of various typical IR complex background images and SIRST datasets show that RBNSM has a better detection performance and clutter suppression ability in complex backgrounds than the seven representative methods.
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    • References (27)
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