[1] [1] Appleby R, Robertson D A, Wikner D. Millimeter wave imaging: a historical review［C］∥Passive and Active Millimeter-Wave Imaging XX. California, USA: SPIE,2017:1—13.

[2] [2] Wang Z, Chang T, Cui H L. Review of active millimeter wave imaging techniques for personnel security screening［J］. IEEE Access,2019,7:148336—148350.

[4] [4] Haworth C D, De Saint-Pern Y, Clark D, et al. Detection and tracking of multiple metallic objects in millimetre-wave images［J］. International Journal of Computer Vision,2007,71(2):183—196.

[5] [5] Wang W, Yang K. A method for millimeter-wave imaging of concealed objects via de-aliasing［C］∥ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Barcelona,Spain:IEEE,2020:4781—4785.

[6] [6] Kemp M C. Millimetre wave and terahertz technology for detection of concealed threats-a review［C］∥2007 Joint 32nd International Conference on Infrared and Millimeter Waves and the 15th International Conference on Terahertz Electronics.Cardiff,UK:IEEE,2007:647—648.

[7] [7] Gu J, Wang Z, Kuen J, et al. Recent advances in convolutional neural networks［J］. Pattern Recognition,2018,77:354—377.

[8] [8] Kumar B, Sharma P, Upadhyay R, et al. Optimization of image processing techniques to detect and reconstruct the image of concealed blade for MMW imaging system［C］∥2016 IEEE International Geoscience and Remote Sensing Symposium(IGARSS).Beijing,P.R.China:IEEE,2016:76—79.

[10] [10] Kowalski M. Kowalski M. Real-time concealed object detection and recognition in passive imaging at 250 GHz［J］. Applied optics,2019,58(12):3134—3140.

[11] [11] Wang S, Ye Z, Wang Y. Real-time dangerous objects detection in millimeter wave images［C］∥Tenth International Conference on Digital Image Processing (ICDIP 2018). International Society for Optics and Photonics.Shanghai,P.R.China:SPIE,2018:262—267.

[13] [13] Liu T, Zhao Y, Wei Y, et al. Concealed object detection for activate millimeter wave image［J］. IEEE Transactions on Industrial Electronics,2019,66(12):9909—9917.

[14] [14] Woo S, Park J, Lee J Y, et al. CBAM: Convolutional block attention module［C］∥Proceedings of the European Conference on Computer Vision(ECCV).Munich,Germany:Springer,2018:3—19.

[15] [15] Hu J, Shen L, Albanie S, et al. Squeeze-and-excitation networks［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence,2019,42(8):2011—2023.

[16] [16] Wang X, Girshick R, Gupta A, et al. Non-local neural networks［C］∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Utah,USA:IEEE,2018:7794—7803.

[17] [17] Guo X, Meng L, Mei L, et al. Multi-focus image fusion with Siamese self-attention network［J］. IET Image Processing,2020,14(7):1339—1346.

[18] [18] Lei Y, Du W, Hu Q. Face sketch-to-photo transformation with multi-scale self-attention GAN［J］. Neurocomputing,2020,396(5):13—23.

[19] [19] Sun F, Li W, Guan Y. Self-attention recurrent network for saliency detection［J］. Multimedia Tools and Applications,2019,78(21):30793—30807.

[20] [20] Lin T Y, RoyChowdhury A, Maji S. Bilinear convolutional neural networks for fine-grained visual recognition［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,40(6):1309—1322.

[22] [22] Kong S, Fowlkes C. Low-rank bilinear pooling for fine-grained classification［C］∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Hawaii,USA:IEEE,2017:365—374.

[23] [23] Wang Y, Ji S, Lu M, et al. Attention boosted bilinear pooling for remote sensing image retrieval［J］. International Journal of Remote Sensing,2020,41(7):2704—2724.

[24] [24] Chetouani A, Treuillet S, Exbrayat M, et al. Classification of engraved pottery sherds mixing deep-learning features by compact bilinear pooling［J］. Pattern Recognition Letters,2020,131:1—7.

[25] [25] Zhang W, Ma K, Yan J, et al. Blind image quality assessment using a deep bilinear convolutional neural network［J］. IEEE Transactions on Circuits and Systems for Video Technology,2020,30(1):36—47.

[26] [26] Pang L, Liu H, Chen Y, et al. Real-time concealed object detection from passive millimeter wave images based on the YOLOv3 algorithm［J］. Sensors,2020,20(6):1678.

[27] [27] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition［DB/OL］. (2014). https:∥arxiv.org/abs/1409.1556

[28] [28] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition［C］∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Nevada,USA:IEEE,2016:770—778.

[29] [29] Russakovsky O, Deng J, Su H, et al. Imagenet large scale visual recognition challenge［J］. International Journal of Computer Vision,2015,115(3):211—252.

[32] [32] Kumar S S, Wang M, Abraham D M, et al. Deep learning-based automated detection of sewer defects in CCTV videos［J］. Journal of Computing in Civil Engineering,2020,34(1):04019047.

[33] [33] Hong F T, Li W H, Zheng W S. Learning to detect important people in unlabelled images for semi-supervised important people detection［C］∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2020:4146—4154.

[34] [34] Guo X, Asif M, Hu A, et al. Design of a low-cost cross-correlation system for aperture synthesis passive millimeter wave imager［C］∥Millimetre Wave and Terahertz Sensors and Technology XI.Berlin,Germany:SPIE,2018:1—4.

[35] [35] Victor N, Lopez D. Sl-lstm: a bi-directional lstm with stochastic gradient descent optimization for sequence labeling tasks in big data［J］. International Journal of Grid and High Performance Computing(IJGHPC),2020,12(3):1—16.

[36] [36] Zhou B, Khosla A, Lapedriza A, et al. Learning deep features for discriminative localization［C］∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Nevada,USA:IEEE,2016:2921—2929.

[37] [37] Hu C, Wang Y. An efficient convolutional neural network model based on object-level attention mechanism for casting defect detection on radiography images［J］. IEEE Transactions on Industrial Electronics,2020,67(12):10922—10930.

[39] [39] Wang Y, Hu C, Chen K, et al. Self-attention guided model for defect detection of aluminium alloy casting on X-ray image［J］. Computers & Electrical Engineering,2020,88:106821.