• Journal of Infrared and Millimeter Waves
  • Vol. 38, Issue 1, 32 (2019)
WANG Chong-Jian1、*, SUN Xiao-Wei2, and YANG Ke-Hu1
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    DOI: 10.11972/j.issn.1001-9014.2019.01.006 Cite this Article
    WANG Chong-Jian, SUN Xiao-Wei, YANG Ke-Hu. A low-complexity method for concealed object detection in active millimeter-wave images[J]. Journal of Infrared and Millimeter Waves, 2019, 38(1): 32 Copy Citation Text show less
    References

    [2] first employed convolutional neural networks(CNNs) and used a dense sliding window method to detect concealed objects. In this paper, the author presents two improvements over Yao's work: 1) Using contextual information to suppress interference and improve detection probability; 2) Using a two-step search method instead of exhaustive search to reduce the computational complexity. To reduce the computational complexity, the author first uses a CNN in vertical direction to filter the interference and obtain the vertical position of the concealed object, then uses another CNN to determine the horizontal position of the concealed object. To make use of big window containing contextual information, the author uses IoG (intersection-over-ground-truth) instead of IoU (Intersection-over-Union) to define positive and negative samples in training and testing process. Experimental results show that the proposed method will make the length of computational time reduced to about 30% of that of the exhaustive search while achieving better detection performance.

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    WANG Chong-Jian, SUN Xiao-Wei, YANG Ke-Hu. A low-complexity method for concealed object detection in active millimeter-wave images[J]. Journal of Infrared and Millimeter Waves, 2019, 38(1): 32
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