Vol. 161
Latest Volume
All Volumes
PIER 176 [2023] PIER 175 [2022] PIER 174 [2022] PIER 173 [2022] PIER 172 [2021] PIER 171 [2021] PIER 170 [2021] PIER 169 [2020] PIER 168 [2020] PIER 167 [2020] PIER 166 [2019] PIER 165 [2019] PIER 164 [2019] PIER 163 [2018] PIER 162 [2018] PIER 161 [2018] PIER 160 [2017] PIER 159 [2017] PIER 158 [2017] PIER 157 [2016] PIER 156 [2016] PIER 155 [2016] PIER 154 [2015] PIER 153 [2015] PIER 152 [2015] PIER 151 [2015] PIER 150 [2015] PIER 149 [2014] PIER 148 [2014] PIER 147 [2014] PIER 146 [2014] PIER 145 [2014] PIER 144 [2014] PIER 143 [2013] PIER 142 [2013] PIER 141 [2013] PIER 140 [2013] PIER 139 [2013] PIER 138 [2013] PIER 137 [2013] PIER 136 [2013] PIER 135 [2013] PIER 134 [2013] PIER 133 [2013] PIER 132 [2012] PIER 131 [2012] PIER 130 [2012] PIER 129 [2012] PIER 128 [2012] PIER 127 [2012] PIER 126 [2012] PIER 125 [2012] PIER 124 [2012] PIER 123 [2012] PIER 122 [2012] PIER 121 [2011] PIER 120 [2011] PIER 119 [2011] PIER 118 [2011] PIER 117 [2011] PIER 116 [2011] PIER 115 [2011] PIER 114 [2011] PIER 113 [2011] PIER 112 [2011] PIER 111 [2011] PIER 110 [2010] PIER 109 [2010] PIER 108 [2010] PIER 107 [2010] PIER 106 [2010] PIER 105 [2010] PIER 104 [2010] PIER 103 [2010] PIER 102 [2010] PIER 101 [2010] PIER 100 [2010] PIER 99 [2009] PIER 98 [2009] PIER 97 [2009] PIER 96 [2009] PIER 95 [2009] PIER 94 [2009] PIER 93 [2009] PIER 92 [2009] PIER 91 [2009] PIER 90 [2009] PIER 89 [2009] PIER 88 [2008] PIER 87 [2008] PIER 86 [2008] PIER 85 [2008] PIER 84 [2008] PIER 83 [2008] PIER 82 [2008] PIER 81 [2008] PIER 80 [2008] PIER 79 [2008] PIER 78 [2008] PIER 77 [2007] PIER 76 [2007] PIER 75 [2007] PIER 74 [2007] PIER 73 [2007] PIER 72 [2007] PIER 71 [2007] PIER 70 [2007] PIER 69 [2007] PIER 68 [2007] PIER 67 [2007] PIER 66 [2006] PIER 65 [2006] PIER 64 [2006] PIER 63 [2006] PIER 62 [2006] PIER 61 [2006] PIER 60 [2006] PIER 59 [2006] PIER 58 [2006] PIER 57 [2006] PIER 56 [2006] PIER 55 [2005] PIER 54 [2005] PIER 53 [2005] PIER 52 [2005] PIER 51 [2005] PIER 50 [2005] PIER 49 [2004] PIER 48 [2004] PIER 47 [2004] PIER 46 [2004] PIER 45 [2004] PIER 44 [2004] PIER 43 [2003] PIER 42 [2003] PIER 41 [2003] PIER 40 [2003] PIER 39 [2003] PIER 38 [2002] PIER 37 [2002] PIER 36 [2002] PIER 35 [2002] PIER 34 [2001] PIER 33 [2001] PIER 32 [2001] PIER 31 [2001] PIER 30 [2001] PIER 29 [2000] PIER 28 [2000] PIER 27 [2000] PIER 26 [2000] PIER 25 [2000] PIER 24 [1999] PIER 23 [1999] PIER 22 [1999] PIER 21 [1999] PIER 20 [1998] PIER 19 [1998] PIER 18 [1998] PIER 17 [1997] PIER 16 [1997] PIER 15 [1997] PIER 14 [1996] PIER 13 [1996] PIER 12 [1996] PIER 11 [1995] PIER 10 [1995] PIER 09 [1994] PIER 08 [1994] PIER 07 [1993] PIER 06 [1992] PIER 05 [1991] PIER 04 [1991] PIER 03 [1990] PIER 02 [1990] PIER 01 [1989]
2018-04-13
Towards Robust Human Millimeter Wave Imaging Inspection System in Real Time with Deep Learning
By
Progress In Electromagnetics Research, Vol. 161, 87-100, 2018
Abstract
With the ever-growing requirements of human security check in public, near-field millimeter wave (MMW) imaging techniques have been developing rapidly in recent years. Due to the lack of MMW images, low resolution and indistinguishable texture in most MMW images, it is still a great challenge to do high performance object detection task on MMW images. In this paper, we propose a novel framework to automatically detect concealed weapons and potential dangerous objects based on a single human millimeter wave image, in which a deep convolutional neural network (CNN) is presented to simultaneously extract features, detect suspicious objects, and give the confidence score. Unlike traditional optical image level solutions, we comprehensively analyze the original MMW data for object representation, incorporate domain-specific knowledge to design and train our network. Moreover, combined with the modern focal loss theory, we devise an effective loss function elaborately to optimize our model. Experimental results on both our dataset and real world data show the effectiveness and improvement of our method compared with the state-of-the-arts.
Citation
Chenyu Liu Ming-Hui Yang Xiao-Wei Sun , "Towards Robust Human Millimeter Wave Imaging Inspection System in Real Time with Deep Learning," Progress In Electromagnetics Research, Vol. 161, 87-100, 2018.
doi:10.2528/PIER18012601
http://www.jpier.org/PIER/pier.php?paper=18012601
References

1. Chen, H. M., S. Lee, R. M. Rao, M. A. Slaman, and P. K. Varshney, "Imaging for concealed weapon detection," IEEE Signal Process. Mag., Vol. 22, No. 2, 52-61, 2005.
doi:10.1109/MSP.2005.1406480

2. Sheen, D. M., D. L. Mcmakin, and T. E. Hal, "Three-dimensional millimeter-wave imaging for concealed weapon detection," IEEE Trans. Microw. Theory Techn., Vol. 49, No. 9, 1581-1592, 2001.
doi:10.1109/22.942570

3. Sheen, D. M., J. L. Fernandes, D. L. Mcmakin, and R. H. Severtsen, "Wide-bandwidth, widebeamwidth, high-resolution, millimeter-wave imaging for concealed weapon detection," SPIE Defense, Security, and Sensing, 871509, 2013.

4. Zhu, Y., M. Yang, L. Wu, Y. Sun, and X. Sun, "Millimeter-wave holographic imaging algorithm with amplitude corrections," Progress In Electromagnetics Research M, Vol. 49, 33-39, 2016.
doi:10.2528/PIERM16050801

5. Lowe, D. G., "Distinctive image features from scale-invariant keypoints," Int. J. Comput. Vis., Vol. 60, No. 2, 91-110, 2004.
doi:10.1023/B:VISI.0000029664.99615.94

6. Dalal, N. and B. Triggs, "Histograms of oriented gradients for human detection," Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 886-893, 2005.

7. Ren, S., K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards real-time object detection with region proposal networks," IEEE Trans. Pattern Anal. Mach. Intell., Vol. 39, No. 6, 1137-1149, 2017.
doi:10.1109/TPAMI.2016.2577031

8. Yao, J., M. Yang, Y. Zhu, L. Wu, and X. Sun, "Using convolutional neural network to localize forbidden object in millimeter-wave image," J. Infrared Millim. Waves, Vol. 36, No. 3, 354-360, 2017.

9. Zhu, Y., M. Yang, L. Wu, Y. Sun, and X. Sun, "Practical millimeter-wave holographic imaging system with good robustness," Chinese Opt. Lett., Vol. 14, No. 10, 43-47, 2016.

10. Girshick, R., J. Donahue, T. Darrell, and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 580-587, 2014.

11. Girshick, R., "Fast R-CNN," Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 1440-1448, 2015.

12. Lecun, Y., Y. Bengio, and G. Hinton, "Deep learning," Nature, Vol. 521, No. 7553, 436-444, 2015.
doi:10.1038/nature14539

13. Yeom, S., D. S. Lee, J. Y. Son, and S. H. Kim, "Concealed object detection using passive millimeter wave imaging," Proc. IEEE Universal Commun. Symp., 383-386, 2010.

14. Xiao, Z., X. Lu, J. Yan, L. Wu, and L. Ren, "Automatic detection of concealed pistols using passive millimeter wave imaging," Proc. IEEE Int. Conf. Imaging Syst. Techn., 1-4, 2015.

15. Tapia, S. L., R. Molina, and N. P. Blanca, "Detection and localization of objects in Passive Millimeter Wave images," Proc. IEEE Signal Process. Conf., 2101-2105, 2016.

16. Redmon, J., S. Divvala, R. Girshick, and A. Farhadi, "You only look once: Unified, real-time object detection," Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 779-788, 2016.

17. Redmon, J. and A. Farhadi, "YOLO9000: Better, faster, stronger," Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 6517-6525, 2017.

18. Krizhevsky, A., I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," Proc. Neural Inform. Process. Syst., 1097-1105, 2012.

19. Lin, T. Y., P. Goyal, R. Girshick, K. He, and P. Dollar, "Focal loss for dense object detection," Proc. IEEE Int. Conf. Comput. Vis., 2999-3007, 2017.

20. Deng, J., W. Dong, R. Socher, and L. J. Li, "ImageNet: A large-scale hierarchical image database," Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 248-255, 2009.

21. Rumelhart, D. E., G. E. Hinton, and R. J. Williams, "Learning internal representations by error propagation," Readings in Cognitive Science, Vol. 1, No. 2, 399-421, 1988.
doi:10.1016/B978-1-4832-1446-7.50035-2

22. Zeiler, M. D. and R. Fergus, "Visualizing and understanding convolutional networks," Proc. 13th Eur. Conf. Comput. Vis., 818-833, 2014.

23. Simonyan, K. and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," Proc. Int. Conf. Learn. Representations, 2015.

24. He, K., X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 770-778, 2016.

25. Jia, Y., E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, "Caffe: Convolutional architecture for fast feature embedding," Proc. 22nd ACM Int. Conf. Multimedia, 675-678, 2014.