[1] Chen Z Q, Zhang L, Jin X. Recent progress on X-ray security inspection technologies[J]. Chinese Science Bulletin, 62, 1350-1365(2017).

[2] Kundegorski M E, Akcay S, Devereux M et al. On using feature descriptors as visual words for object detection within X-ray baggage security screening[C], 1-6(2016).

[3] 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).

[4] 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).

[5] Zhu X K, Zhang J T, Chen X P et al. AMOD-net: attention-based multi-scale object detection network for X- ray baggage security inspection[C], 27-32(2021).

[6] Wang C Y, Bochkovskiy A, Liao H Y M. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[EB/OL]. http://arxiv.org/abs/2207.02696v1

[7] Hao B Q, Fan Y G, Song Z H. Deep transfer learning-based pulsed eddy current thermography for crack defect detection[J]. Acta Optica Sinica, 43, 0415002(2023).

[8] Bochkovskiy A, Wang C Y, Liao H Y M. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. http://arxiv.org/abs/2004.10934v1

[9] Lin M, Zeng F, Liu X X et al. Lithography hotspot detection based on improved YOLOv3[J]. Acta Optica Sinica, 43, 2315001(2023).

[10] Guo S X, Zhang L. Yolo-C: one-stage network for prohibited items detection within X-ray images[J]. Laser & Optoelectronics Progress, 58, 0810003(2021).

[11] Yu Q H, Wu Q, Liu H Q. Research on X-ray contraband detection and overlapping target detection based on convolutional network[C], 736-741(2022).

[12] Xiang J, Li G Q, Wu J et al. Detection of threat items in X-ray security inspection images based on improved YOLOv5s algorithm[J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 35, 943-951(2023).

[13] Zhang X Z, Shen T, Xu D. Remote-sensing image object detection based on improved YOLOv8 algorithm[J]. Laser & Optoelectronics Progress, 61, 1028001(2024).

[14] Zhang L F, Tian Y. Improved YOLOv8 multi-scale and lightweight vehicle object detection algorithm[J]. Computer Engineering and Applications, 60, 129-137(2024).

[15] Lu L. Improved YOLOv8 detection algorithm in security inspection image[EB/OL]. https://arxiv.org/abs/2308.06452

[16] Wang H Q, Wei P X. X-ray image contraband detection based on improved YOLOv8[J/OL]. Radio Engineering, 1-9. https://link.cnki.net/urlid/13.1097.tn.20240111.1531.002

[17] Wang C Y, Bochkovskiy A, Liao H Y M. Scaled-YOLOv4: scaling cross stage partial network[C], 13024-13033(2021).

[18] Liu S, Qi L, Qin H F et al. Path aggregation network for instance segmentation[C], 8759-8768(2018).

[19] Wang Y, Tao Z S, Shi X Y et al. Apple target detection method with different ripeness based on improved YOLOv5s[J]. Journal of Nanjing Agricultural University, 47, 602-611(2024).

[20] Lin T Y, Dollár P, Girshick R et al. Feature pyramid networks for object detection[C], 936-944(2017).

[21] Tan M X, Pang R M, Le Q V. EfficientDet: scalable and efficient object detection[C], 10778-10787(2020).

[22] Liu Y C, Shao Z R, Hoffmann N. Global attention mechanism: retain information to enhance channel-spatial interactions[EB/OL], 12. https://arxiv.org/abs/2112.05561

[23] Tolstikhin I, Houlsby N, Kolesnikov A et al. MLP-Mixer: an all-MLP architecture for vision[EB/OL]. https://arxiv.org/abs/2105.01601

[24] Mery D, Riffo V, Zscherpel U et al. GDXray: the database of X-ray images for nondestructive testing[J]. Journal of Nondestructive Evaluation, 34, 42(2015).

[25] 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).

[26] 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).

[27] Wang B Y, Zhang L B, Wen L Y et al. Towards real-world prohibited item detection: a large-scale X-ray benchmark[C], 5392-5401(2021).

[28] Zheng Z H, Wang P, Liu W et al. Distance-IoU loss: faster and better learning for bounding box regression[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 12993-13000(2020).

[29] Girshick R. Fast R-CNN[C], 1440-1448(2015).

[30] Xue J D, Zhu J J, Zhang J et al. Object detection in optical remote sensing images based on FFC-SSD model[J]. Acta Optica Sinica, 42, 1210002(2022).

[31] Li C Y, Li L L, Jiang H L et al. YOLOv6: a single-stage object detection framework for industrial applications[EB/OL]. https://arxiv.org/abs/2209.02976