[3] McCarley J S, Kramer A F, Wickens C D, et al. Visual skills in airport-security screening[J]. Psychological Science, 2004, 15(5): 302-306.
[5] Mery D, Mondragon G, Riffo V, et al. Detection of regular objects in baggage using multiple X-ray views[J]. Insight-Non-Destructive Testing and Condition Monitoring, 2013, 55(1): 16-20.
[6] Michel S, Mendes M, de Ruiter J C, et al. Increasing X-ray image interpretation competency of cargo security screeners[J]. International Journal of Industrial Ergonomics, 2014, 44(4): 551-560.
[9] ZHAO B, Wolter S, Greenberg J A. Application of machine learning to x-ray diffraction-based classification[C]//Anomaly Detection and Imaging with X-Rays(ADIX) III. International Society for Optics and Photonics, 2018, 10632: 1063205.
[10] Gaus Y F A, Bhowmik N, Breckon T P. On the use of deep learning for the detection of firearms in x-ray baggage security imagery[C]//2019 IEEE International Symposium on Technologies for Homeland Security (HST), 2019: 1-7.
[11] Franzel T, Schmidt U, Roth S. Object detection in multi-view X-ray images[C]//Joint DAGM (German Association for Pattern Recognition) and OAGM Symposium, 2012: 144-154.
[14] WANG L, GUO S, HUANG W, et al. Places205-vggnet models for scene recognition[J/OL]. arXiv preprint arXiv:1508.01667, 2015.
[15] Ballester P, Araujo R M. On the performance of GoogLeNet and AlexNet applied to sketches[C]//Thirtieth AAAI Conference on Artificial Intelligence, 2016, 30(1): doi: https://doi.org/10.1609/aaai.v30i1.10171.
[16] Haque M F, Lim H Y, Kang D S. Object detection based on VGG with ResNet network[C]//2019 International Conference on Electronics, Information, and Communication (ICEIC) of IEEE, 2019: 1-3(doi: 10.23919/ELINFOCOM.2019.8706476).
[17] ZOU Z, SHI Z, GUO Y, et al. Object detection in 20 years: a survey[J/OL]. arXiv preprint arXiv:1905.05055, 2019.
[18] CHEN C, LIU M Y, Tuzel O, et al. R-CNN for small object detection[C]//Asian Conference on Computer Vision, 2016: 214-230.
[19] Girshick R. Fast r-cnn[C]//Proceedings of the IEEE International Conference on Computer Vision, 2015: 1440-1448.
[20] R Ren S, He K, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6): 1137-1149.
[21] ZHANG Y, KONG W, LI D, et al. On using XMC R-CNN model for contraband detection within X-ray baggage security images[J]. Mathematical Problems in Engineering, 2020, 2020: 1-14.
[22] Sigman J B, Spell G P, LIANG K J, et al. Background adaptive faster R-CNN for semi-supervised convolutional object detection of threats in x-ray images[C]//Anomaly Detection and Imaging with X-Rays (ADIX) V, 2020, 11404: 1140404.
[23] Papageorgiou C P, Oren M, Poggio T. A general framework for object detection[C]//Sixth International Conference on Computer Vision (IEEE Cat. No. 98CH36271) of IEEE, 1998: 555-562.
[24] LIU Z, LI J, SHU Y, et al. Detection and recognition of security detection object based on YOLO9000[C]//2018 5th International Conference on Systems and Informatics (ICSAI)of IEEE, 2018: 278-282.
[25] Galvez R L, Dadios E P, Bandala A A, et al. YOLO-based Threat Object Detection in X-ray Images[C]//2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), 2019: 1-5.
[29] GE Z, LIU S, WANG F, et al. YOLOX: Exceeding Yolo series in 2021[J/OL]. arXiv preprint arXiv:2107.08430, 2021.
[30] WANG C Y, LIAO H, WU Y H, et al. CSPNet: a new backbone that can enhance learning capability of CNN[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020: 390-391.
[31] Woo S, Park J, Lee J Y, et al. Cbam: convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018: 3-19.
[32] MIAO C, XIE L, WAN F, et al. SiXray: a large-scale security inspection x-ray benchmark for prohibited item discovery in overlapping images[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 2119-2128.