[1] 李涛, 张良, 何建安, 等. 基于太赫兹技术在线快速识别邮件隐匿危险品[J]. 激光与光电子学进展, 2019, 56(23): 233001.

    Li T, Zhang L, He J A, et al. Rapid online identification of hazardous substances in mail using terahertz technology[J]. Laser & Optoelectronics Progress, 2019, 56(23): 233001.

[2] Singh S, Singh M. Explosives detection systems (EDS) for aviation security[J]. Signal Processing, 2003, 83(1): 31-55.

[3] Mery D. Computer vision technology for X-ray testing[J]. Insight - Non-Destructive Testing and Condition Monitoring, 2014, 56(3): 147-155.

[4] Roomi M M. Detection of concealed weapons in X-ray images using fuzzy K-NN[J]. International Journal of Computer Science, Engineering and Information Technology, 2012, 2(2): 187-196.

[5] Kundegorski ME, AkcayS, DevereuxM, et al. On using feature descriptors as visual words for object detection within X-ray baggage security screening[C] //7th International Conference on Imaging for Crime Detection and Prevention (ICDP 2016), Madrid, Spain. London: Institution of Engineering and Technology, 2016: 1- 6.

[6] Piñol DC, Reyes E JM. Automatic handgun detection in X-ray images using bag of words model with selective search[EB/OL]. (2019-03-04)[ 2020-08-02]. https://arxiv.org/abs/1903.01322.

[7] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.

[8] JaccardN, Rogers TW, Morton EJ, et al. Using deep learning on X-ray images to detect threats[D]. London: University College London, 2016.

[9] SimonyanK, ZissermanA. Very deep convolutional networks for large-scale image recognition[EB/OL]. (2015-04-10)[ 2020-08-02]. https://arxiv.org/abs/1409.1556v6.

[10] AkcayS, Kundegorski ME, DevereuxM, et al.Transfer learning using convolutional neural networks for object classification within X-ray baggage security imagery[C] //2016 IEEE International Conference on Image Processing (ICIP), September 25-28, 2016, Phoenix, AZ, USA.New York: IEEE Press, 2016: 1057- 1061.

[11] Akcay S, Kundegorski M E, Willcocks C G, et al. Using deep convolutional neural network architectures for object classification and detection within X-ray baggage security imagery[J]. IEEE Transactions on Information Forensics and Security, 2018, 13(9): 2203-2215.

[12] GirshickR, DonahueJ, DarrellT, et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C] //2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA.New York: IEEE Press, 2014: 580- 587.

[13] Dai JF, LiY, He KM, et al. R-FCN: object detection via region-based fully convolutional networks[EB/OL]. (2016-06-21)[2020-08-02]. https://arxiv.org/abs/1605.06409v2.

[14] Cañizares P C, Merayo M G, Núñez A. FORTIFIER: a formal distributed framework to improve the detection of threatening objects in baggage[J]. Journal of Information and Telecommunication, 2018, 2(1): 2-18.

[15] AkcayS, Atapour-AbarghoueiA, Breckon TP. GANomaly: semi-supervised anomaly detection via adversarial training[C] //Jawahar C, Li H, Mori G, et al. Computer Vision - ACCV 2018, 2018, 11363: 622- 637. 10.1007/978-3-030-20893-6_39.

[16] Gaus Y FA, BhowmikN, AkçayS, et al. Evaluation of a dual convolutional neural network architecture for object-wise anomaly detection in cluttered X-ray security imagery[C] //2019 International Joint Conference on Neural Networks (IJCNN), July 14-19, 2019, Budapest, Hungary.New York: IEEE Press, 2019: 1- 8.

[17] Miao CJ, Xie LX, WanF, et al.SIXray: a large-scale security inspection X-ray benchmark for prohibited item discovery in overlapping images[C] //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA. New York: IEEE Press, 2019: 2114- 2123.

[18] He KM, Zhang XY, Ren SQ, 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 Press, 2016: 770- 778.

[19] RedmonJ, FarhadiA. YOLOv3: an incremental improvement[EB/OL]. (2018-04-08)[2020-08-02]. https://arxiv.org/abs/1804.02767.

[20] Lin TY, DollárP, GirshickR, 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 Press, 2017: 936- 944.

[21] 李成跃, 姚剑敏, 林志贤, 等. 基于改进YOLO轻量化网络的目标检测方法[J]. 激光与光电子学进展, 2020, 57(14): 141003.

    Li C Y, Yao J M, Lin Z X, et al. Object detection method based on improved YOLO lightweight network[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141003.

[22] 郭进祥, 刘立波, 徐峰, 等. 基于YOLO_v3的机场场面飞机检测方法[J]. 激光与光电子学进展, 2019, 56(19): 191003.

    Guo J X, Liu L B, Xu F, et al. Airport scene aircraft detection method based on YOLO_v3[J]. Laser & Optoelectronics Progress, 2019, 56(19): 191003.

[23] Liu YD, Wang YT, Wang SW, et al. CBNet: a novel composite backbone network architecture for object detection[EB/OL]. (2019-09-09)[2020-08-02]. https:∥arxiv.org/abs/1909.03625.

[24] Cai ZW, VasconcelosN. Cascade R-CNN: delving into high quality object detection[C] //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE Press, 2018: 6154- 6162.