[1] Chang Q Q, Chen J M, Li W J. Dangerous goods detection technology based on X-ray images in urban rail transit security inspection[J]. Urban Mass Transit, 25, 205-209(2022).

[2] Chen Z Q, Zhang L, Jin X. Recent progress on X-ray security inspection technologies[J]. Chin Sci Bull, 62, 1350-1364(2017).

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

[4] Liang T F, Zhang N F, Zhang Y X et al. Summary of research progress on application of prohibited item detection in X-ray images[J]. Comput Eng Appl, 57, 74-82(2021).

[5] 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-Destr Test Cond Monit, 59, 85-92(2017).

[6] Wang Y, Zou W H, Yang X M et al. X-ray image illegal object classification based on computer vision[J]. Chin J Liq Cryst Disp, 32, 287-293(2017).

[7] 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). https://doi.org/10.1109/ICIT.2013.6505833

[8] Dai J F, Li Y, He K M et al. R-FCN: object detection via region-based fully convolutional networks[C], 379-387(2016).

[9] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Trans Pattern Anal Mach Intell, 39, 1137-1149(2017).

[10] Liu W, Anguelov D, Erhan D et al. SSD: single shot multibox detector[C], 21-37(2016). https://doi.org/10.1007/978-3-319-46448-0_2

[11] Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection[J]. IEEE Trans Pattern Anal Mach Intell, 42, 318-327(2020).

[12] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection[C], 779-788(2016). https://doi.org/10.1109/CVPR.2016.91

[13] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C], 6517-6525(2017). https://doi.org/10.1109/CVPR.2017.690

[14] Redmon J, Farhadi A. Yolov3: an incremental improvement[Z](2018). https://arxiv.org/abs/1804.02767

[15] Bochkovskiy A, Wang C Y, Liao H Y M. YOLOv4: optimal speed and accuracy of object detection[Z](2020). https://arxiv.org/abs/2004.10934

[16] Ge Z, Liu S T, Wang F et al. YOLOX: exceeding YOLO series in 2021[Z](2021). https://arxiv.org/abs/2107.08430

[17] Zhu Z M, Zhu Y, Wang H R et al. FDTNet: enhancing frequency-aware representation for prohibited object detection from X-ray images via dual-stream transformers[J]. Eng Appl Artif Intell, 133, 108076(2024).

[18] Ahmed A, Velayudhan D, Hassan T et al. Enhancing security in X-ray baggage scans: a contour-driven learning approach for abnormality classification and instance segmentation[J]. Eng Appl Artif Intell, 130, 107639(2024).

[19] Dong Y S, Guo J Y, Li M Z et al. X-ray prohibited items detection based on inverted bottleneck and light convolution block attention module[J]. J Front Comput Sci Technol, 18, 1259-1270(2024).

[20] Zhou Y T, Cao K Y, Piao J C. Fine-YOLO: a simplified X-ray prohibited object detection network based on feature aggregation and normalized Wasserstein distance[J]. Sensors (Basel), 24, 3588(2024).

[21] Han L, Ma C H, Liu Y et al. SC-Lite: an efficient lightweight model for real-time X-ray security check[J]. IEEE Access, 12, 103419-103432(2024).

[22] Han L, Ma C H, Liu Y et al. SC-YOLOv8: a security check model for the inspection of prohibited items in X-ray images[J]. Electronics, 12, 4208(2023).

[23] Wang A L, Yuan P F, Wu H B et al. Improved YOLOv8 for dangerous goods detection in X-ray security images[J]. Electronics, 13, 3238(2024).

[24] Wang Z S, Wang X H, Shi Y T et al. Lightweight detection method for X-ray security inspection with occlusion[J]. Sensors, 24, 1002(2024).

[25] Chaple G, Daruwala R D. Design of Sobel operator based image edge detection algorithm on FPGA[C], 788-792(2014). https://doi.org/10.1109/ICCSP.2014.6949951

[26] Lin M, Chen Q, Yan S C. Network in network[Z](2014). https://arxiv.org/abs/1312.4400

[27] Yin X Y, Goudriaan J, Lantinga E A et al. A flexible sigmoid function of determinate growth[J]. Ann Bot, 91, 361-371(2003).

[28] Yang F H, Jiang R Q, Yan Y et al. Dual-mode learning for multi-dataset X-ray security image detection[J]. IEEE Trans Inf Foren Secur, 19, 3510-3524(2024).

[29] 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). https://doi.org/10.1145/3394171.3413828

[30] Guo S C, Jin Q Z, Wang H Z et al. Learnable gated convolutional neural network for semantic segmentation in remote-sensing images[J]. Remote Sens, 11, 1922(2019).

[31] Arora R, Basu A, Mianjy P et al. Understanding deep neural networks with rectified linear units[C](2018).

[32] Ding M Y, Xiao B, Codella N et al. DaViT: dual attention vision transformers[C], 74-92(2022). https://doi.org/10.1007/978-3-031-20053-3_5

[33] Sun G Q, Pan Y Z, Kong W K et al. DA-TransUNet: integrating spatial and channel dual attention with transformer U-net for medical image segmentation[J]. Front Bioeng Biotechnol, 12, 1398237(2024).

[34] Wang H N, Cao P, Wang J Q et al. UCTransNet: rethinking the skip connections in U-Net from a channel-wise perspective with transformer[C], 2441-2449(2022). https://doi.org/10.1609/aaai.v36i3.20144

[35] Lei Ba J, Kiros J R, Hinton G E. Layer normalization[Z](2016). https://arxiv.org/abs/1607.06450

[36] Hendrycks D, Gimpel K. Gaussian error linear units (GELUs)[Z](2023). https://arxiv.org/abs/1606.08415v4

[37] Chollet F. Xception: deep learning with depthwise separable convolutions[C], 1800-1807(2017). https://doi.org/10.1109/CVPR.2017.195

[38] Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions[C](2016).

[39] Yu F, Koltun V, Funkhouser T. Dilated residual networks[C], 636-644(2017). https://doi.org/10.1109/CVPR.2017.75

[40] Chen L W, Gu L, Zheng D Z et al. Frequency-adaptive dilated convolution for semantic segmentation[C], 3414-3425(2024). https://doi.org/10.1109/CVPR52733.2024.00328

[41] Wahab M F, Gritti F, O'Haver T C. Discrete Fourier transform techniques for noise reduction and digital enhancement of analytical signals[J]. TrAC Trends Anal Chem, 143, 116354(2021).

[42] 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). https://doi.org/10.1109/CVPR.2019.00222

[43] Li C Y, Li L L, Jiang H L et al. YOLOv6: a single-stage object detection framework for industrial applications[Z](2022). https://arxiv.org/abs/2209.02976

[44] Liu C, Hong Z Y, Yu W H et al. An efficient helmet wearing detection method based on YOLOv7-tiny[C], 92-99(2023). https://doi.org/10.1145/3635638.3635652

[45] Wang C Y, Yeh I H, Mark Liao H Y. YOLOv9: learning what you want to learn using programmable gradient information[C], 1-21(2024). https://doi.org/10.1007/978-3-031-72751-1_1

[46] Wang A, Chen H, Liu L H et al. Yolov10: real-time end-to-end object detection[C](2024).