[1] Mery D, Saavedra D, Prasad M. X-ray baggage inspection with computer vision: a survey[J]. IEEE Access, 8, 145620-145633(2020).

[2] Zentai G. X-ray imaging for homeland security[J]. International Journal of Signal and Imaging Systems Engineering, 3, 13-20(2010).

[3] Akcay S, Breckon T. Towards automatic threat detection: a survey of advances of deep learning within X-ray security imaging[J]. Pattern Recognition, 122, 108245(2022).

[4] Michel S, Koller S M, de Ruiter J C et al. Computer-based training increases efficiency in X-ray image interpretation by aviation security screeners[C], 201-206(2007).

[5] Mery D, Svec E, Arias M et al. Modern computer vision techniques for X-ray testing in baggage inspection[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47, 682-692(2017).

[6] Zhang Y K, Su Z G, Zhang H G et al. Multi-scale prohibited item detection in X-ray security image[J]. Journal of Signal Processing, 36, 1096-1106(2020).

[7] Baştan M, Yousefi M R, Breuel T M. Visual words on baggage X-ray images[M]. Real P, Diaz-Pernil D, Molina-Abril H, et al. Computer analysis of images and patterns. Lecture notes in computer science, 360-368(2011).

[8] Turcsany D, Mouton A, Breckon T P. Improving feature-based object recognition for X-ray baggage security screening using primed visualwords[C], 1140-1145(2013).

[9] Akçay S, Kundegorski M E, Devereux M et al. Transfer learning using convolutional neural networks for object classification within X-ray baggage security imagery[C], 1057-1061(2016).

[10] 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, 13, 2203-2215(2018).

[11] Baştan M. Multi-view object detection in dual-energy X-ray images[J]. Machine Vision and Applications, 26, 1045-1060(2015).

[12] Akcay S, Breckon T P. An evaluation of region based object detection strategies within X-ray baggage security imagery[C], 1337-1341(2017).

[13] Liu J Y, Leng X X, Liu Y. Deep convolutional neural network based object detector for X-ray baggage security imagery[C], 1757-1761(2019).

[14] Mery D, Riffo V, Zuccar I et al. Object recognition in X-ray testing using an efficient search algorithm in multiple views[J]. Insight-Non-Destructive Testing and Condition Monitoring, 59, 85-92(2017).

[15] Mery D. Automated detection in complex objects using a tracking algorithm in multiple X-ray views[C], 41-48(2011).

[16] Bastan M, Byeon W, Breuel T. Object recognition in multi-view dual energy X-ray images[C](2013).

[17] Hassan T, Khan S H, Akcay S et al. Cascaded structure tensor framework for robust identification of heavily occluded baggage items from multi-vendor X-ray scans[EB/OL]. https://arxiv.org/abs/1912.04251

[18] Miao C J, Xie L X, Wan F et al. SIXray: a large-scale security inspection X-ray benchmark for prohibited item discovery in overlapping images[C], 2114-2123(2019).

[19] Shao F T, Liu J, Wu P et al. Exploiting foreground and background separation for prohibited item detection in overlapping X-Ray images[J]. Pattern Recognition, 122, 108261(2022).

[20] Wei Y L, Tao R S, Wu Z J et al. Occluded prohibited items detection: an X-ray security inspection benchmark and de-occlusion attention module[C], 138-146(2020).

[21] Guo R H, Zhang L, Yang Y et al. X-ray image controlled knife detection and recognition based on improved SSD[J]. Laser & Optoelectronics Progress, 58, 0404001(2021).

[22] Wang Y X, Zhang L. Dangerous goods detection based on multi-scale feature fusion in security images[J]. Laser & Optoelectronics Progress, 58, 0810012(2021).

[23] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 3-19(2018).

[24] Zhang H Y, Cisse M, Dauphin Y N et al. Mixup: beyond empirical risk minimization[EB/OL]. https://arxiv.org/abs/1710.09412

[25] Solovyev R, Wang W M, Gabruseva T. Weighted boxes fusion: Ensembling boxes from different object detection models[J]. Image and Vision Computing, 107, 104117(2021).

[26] Glenn J, Alex S, Jirka B et al. ultralytics/yolov5: v5.0-YOLOv5 -P6 1280 models, AWS, Supervise. ly and YouTube integrations[EB/OL]. https://github.com/ultralytics/yolov5

[27] Neubeck A, Van Gool L. Efficient non-maximum suppression[C], 850-855(2006).

[28] Tao R S, Wei Y L, Jiang X J et al. Towards real-world X-ray security inspection: a high-quality benchmark and lateral inhibition module for prohibited items detection[C], 10903-10912(2021).

[29] Redmon J, Farhadi A. YOLOv3: an incremental improvement[EB/OL]. https://arxiv.org/abs/1804.02767

[30] Bochkovskiy A, Wang C Y, Liao H Y M. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. https://arxiv.org/abs/2004.10934

[31] Wu H B, Wei X Y, Liu M H et al. Improved YOLOv4 for dangerous goods detection in X-ray inspection combined with atrous convolution and transfer learning[J]. Chinese Optics, 14, 1417-1425(2021).

[32] Liu W, Anguelov D, Erhan D et al. SSD: single shot MultiBox detector[M]. Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9905, 21-37(2016).

[33] Tian Z, Shen C H, Chen H et al. FCOS: fully convolutional one-stage object detection[C], 9626-9635(2019).