volume | PIER Journals

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.

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 Google Scholar

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 Google Scholar

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. Google Scholar

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 Google Scholar

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 Google Scholar

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

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 Google Scholar

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. Google Scholar

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. Google Scholar

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. Google Scholar

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

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

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. Google Scholar

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. Google Scholar

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. Google Scholar

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. Google Scholar

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

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

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. Google Scholar

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. Google Scholar

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 Google Scholar

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

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

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. Google Scholar

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. Google Scholar

Dr. Chenyu Liu

Mr. Ming-Hui Yang

Dr. Xiao-Wei Sun