[1] Sheen D M. McMakin D L, Hall T E. Three-dimensional millimeter-wave imaging for concealed weapon detection[J]. IEEE Transactions on Microwave Theory and Techniques, 49, 1581-1592(2001).

[2] Zhu Y K, Yang M H, Wu L et al. Millimeter-wave holographic imaging algorithm with amplitude corrections[J]. Progress in Electromagnetics Research M, 49, 33-39(2016).

[3] Zhu Y K, Yang M H, Wu L et al. Practical millimeter-wave holographic imaging system with good robustness[J]. Chinese Optics Letters, 14, 101101(2016). http://www.opticsjournal.net/Articles/Abstract?aid=OJ160914000160Zv3y6B

[4] Albelwi S, Mahmood A. Automated optimal architecture of deep convolutional neural networks for image recognition. [C]∥2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), December 18-20, 2016, Anaheim, CA, USA. New York: IEEE, 53-60(2016).

[5] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 770-778(2016).

[6] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions. [C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 7298594(2015).

[7] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. [C]∥Proceedings of the 32nd International Conference on International Conference on Machine Learning, July 6-11, 2015, Lille, France. Massachusetts: JMLR.org, 37, 448-456(2015).

[8] Szegedy C, Vanhoucke V, Ioffe S. et al. Rethinking the inception architecture for computer vision. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2818-2826(2016).

[9] Szegedy C, Ioffe S. -08-23)[2018-12-01]. https:∥arxiv., org/abs/1602, 07261(2016).

[10] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017). http://www.ncbi.nlm.nih.gov/pubmed/27295650

[11] Liu W, Anguelov D, Erhan D et al. SSD: single shot multibox detector[M]. ∥Leibe B, Matas J, Sebe N, et al. Lecture Notes in Computer Science. Cham: Springer, 9905, 21-37(2016).

[12] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 779-788(2016).

[13] Redmon J, Farhadi A. YOLO9000: better, faster, stronger. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 6517-6525(2017).

[14] Redmon J. -04-08)[2018-12-01]. https:∥arxiv., org/abs/1804, 02767(2018).

[15] Shrivastava A, Gupta A, Girshick R. Training region-based object detectors with online hard example mining. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 761-769(2016).

[16] Lin T Y, Dollár P, Girshick R et al. Feature pyramid networks for object detection. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 936-944(2017).

[17] Azulay A. -03-30)[2018-12-01]. https:∥arxiv., org/abs/1805, 12177(2018).

[18] Ronneberger O, Fischer P, Brox T. U-Net:convolutional networks for biomedical image segmentation[M]. ∥Navab N, Hornegger J, Wells W, et al. Lecture Notes in Computer Science. Cham: Springer, 9351, 234-241(2015).

[19] Jia Y Q, Shelhamer E, Donahue J et al. Caffe: convolutional architecture for fast feature embedding. [C]∥Proceedings of the 22nd ACM International Conference on Multimedia, November 3-7, 2014, Orlando, Florida, USA. New York: ACM, 675-678(2014).

[20] Huang G, Liu Z. Maaten L V D, et al. Densely connected convolutional networks. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2261-2269(2017).

[21] Veit A, Wilber M. -10-27)[2018-12-01]. https:∥arxiv.org/abs/1605.06431v1.(2016).