[1] Zhang Y, Zhu G Y, Shi T J et al. Small object detection in remote sensing images based on feature fusion and attention[J]. Acta Optica Sinica, 42, 2415001(2022).

[2] He Z F, Chen G C, Chen J S et al. Multi-scale feature fusion lightweight real-time infrared pedestrian detection at night[J]. Chinese Journal of Lasers, 49, 1709002(2022).

[3] 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).

[4] Cheng L, Jing C. X-ray image rotating object detection based on improved YOLOv7[J]. Journal of Graphics, 44, 324-334(2023).

[5] Wu L Q, Chu X T, Yang H T et al. X-ray detection of prohibited items based on improved YOLOX[J]. Infrared Technology, 45, 427-435(2023).

[6] Wang Y, Wang C C, Zhao Z C et al. Improved security contraband detection based on YOLOv8[J/OL]. Control Engineering of China, 1-6. https://doi.org/10.14107/j.cnki.kzgc.20230586

[7] Li S, Yasenjiang M. Improved YOLOv7 X-ray image real-time detection of prohibited items[J]. Computer Engineering and Applications, 59, 193-200(2023).

[8] Wang C Y, Yeh I H, Liao H Y M. YOLOv9: learning what you want to learn using programmable gradient information[EB/OL]. https://arxiv.org/abs/2402.13616

[9] Wang W H, Dai J F, Chen Z et al. InternImage: exploring large-scale vision foundation models with deformable convolutions[C], 14408-14419(2023).

[10] Wang C Y, Liao H Y M, Wu Y H et al. CSPNet: a new backbone that can enhance learning capability of CNN[C], 1571-1580(2020).

[11] 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).

[12] Zhang X D, Zeng H, Guo S et al. Efficient long-range attention network for image super-resolution[EB/OL]. http://arxiv.org/abs/2203.06697v1

[13] Ding X H, Zhang X Y, Ma N N et al. RepVGG: making VGG-style ConvNets great again[C], 13728-13737(2021).

[14] Zhu X Z, Hu H, Lin S et al. Deformable ConvNets V2: more deformable, better results[C], 9300-9308(2019).

[15] Zhang H, Xu C, Zhang S J. Inner-IoU: more effective intersection over union loss with auxiliary bounding box[EB/OL]. http://arxiv.org/abs/2311.02877v4

[16] Zhang H, Zhang S J. Shape-IoU: more accurate metric considering bounding box shape and scale[EB/OL]. https://arxiv.org/abs/2312.17663

[17] 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).

[18] 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).

[19] Zhao C R, Zhu L, Dou S G et al. Detecting overlapped objects in X-ray security imagery by a label-aware mechanism[J]. IEEE Transactions on Information Forensics and Security, 17, 998-1009(2022).

[20] 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

[21] Chen L C, Papandreou G, Kokkinos I et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2018).

[22] Zhang Y F, Ren W Q, Zhang Z et al. Focal and efficient IoU loss for accurate bounding box regression[J]. Neurocomputing, 506, 146-157(2022).

[23] Gevorgyan Z. SIoU loss: more powerful learning for bounding box regression[EB/OL]. http://arxiv.org/abs/2205.12740v1

[24] Dong Y S, Guo J Y, Li M Z et al. X-ray contraband detection based on reverse bottleneck and LCBAM design[J]. Journal of Frontiers of Computer Science and Technology, 18, 1259-1270(2024).

[25] 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

[26] Zhao Y A, Lü W Y, Xu S L et al. DETRs beat YOLOs on real-time object detection[EB/OL]. http://arxiv.org/abs/2304.08069v3