[1] |
陈志强, 张丽, 金鑫. X射线安全检查技术研究新进展[J]. 科学通报,2017,62(13):1350-1365. doi: 10.1360/N972016-00698
CHEN ZH Q, ZHANG L, JIN X. Recent progress on X-ray security inspection technologies[J]. Chinese Science Bulletin, 2017, 62(13): 1350-1365. (in Chinese) doi: 10.1360/N972016-00698 |
[2] |
CAO S S, LIU Y H, SONG W W, et al.. Toward human-in-the-loop prohibited item detection in X-ray baggage images[C]. Proceedings of 2019 Chinese Automation Congress (CAC), IEEE, 2019: 4360-4364. |
[3] |
LYU SH J, TU X, LU Y. X-Ray image classification for parcel inspection in high-speed sorting line[C]. Proceedings of the 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), IEEE, 2018: 1-5. |
[4] |
费彬, 孙京阳, 张俊举, 等. 基于稀疏处理的多能X射线分离成像[J]. 光学 精密工程,2017,25(4):1106-1111. doi: 10.3788/OPE.20172504.1106
FEI B, SUN J Y, ZHANG J J, et al. Separation of multi-energy X-ray imaging based on sparse processing[J]. Optics and Precision Engineering, 2017, 25(4): 1106-1111. (in Chinese) doi: 10.3788/OPE.20172504.1106 |
[5] |
王旖旎. 基于Inception V3的图像状态分类技术[J]. 液晶与显示,2020,35(4):389-394. doi: 10.3788/YJYXS20203504.0389
WANG Y N. Image classification technology based on inception V3[J]. Chinese Journal of Liquid Crystals and Displays, 2020, 35(4): 389-394. (in Chinese) doi: 10.3788/YJYXS20203504.0389 |
[6] |
CHOUAI M, MERAH M, SANCHO-GOMEZ J L, et al. Supervised feature learning by adversarial autoencoder approach for object classification in dual X-Ray image of luggage[J]. Journal of Intelligent Manufacturing, 2020, 31(5): 1101-1112. doi: 10.1007/s10845-019-01498-5 |
[7] |
张万征, 胡志坤, 李小龙. 基于LeNet-5的卷积神经图像识别算法[J]. 液晶与显示,2020,35(5):486-490. doi: 10.3788/YJYXS20203505.0486
ZHANG W ZH, HU ZH K, LI X L. Convolutional neural image recognition algorithm based on LeNet-5[J]. Chinese Journal of Liquid Crystals and Displays, 2020, 35(5): 486-490. (in Chinese) doi: 10.3788/YJYXS20203505.0486 |
[8] |
刘恋秋. 基于深度卷积生成对抗网络的图像识别算法[J]. 液晶与显示,2020,35(4):383-388. doi: 10.3788/YJYXS20203504.0383
LIU L Q. Image recognition algorithms based on deep convolution generative adversarial network[J]. Chinese Journal of Liquid Crystals and Displays, 2020, 35(4): 383-388. (in Chinese) doi: 10.3788/YJYXS20203504.0383 |
[9] |
龚希, 吴亮, 谢忠, 等. 融合全局和局部深度特征的高分辨率遥感影像场景分类方法[J]. 光学学报,2019,39(3):0301002. doi: 10.3788/AOS201939.0301002
GONG X, WU L, XIE ZH, et al. Classification method of high-resolution remote sensing scenes based on fusion of global and local deep features[J]. Acta Optica Sinica, 2019, 39(3): 0301002. (in Chinese) doi: 10.3788/AOS201939.0301002 |
[10] |
贠卫国, 史其琦, 王民. 基于深度卷积神经网络的多特征融合的手势识别[J]. 液晶与显示,2019,34(4):417-422. doi: 10.3788/YJYXS20193404.0417
YUN W G, SHI Q Q, WANG M. Multi-feature fusion gesture recognition based on deep convolutional neural network[J]. Chinese Journal of Liquid Crystals and Displays, 2019, 34(4): 417-422. (in Chinese) doi: 10.3788/YJYXS20193404.0417 |
[11] |
LIU J Y, LENG X X, LIU Y. Deep convolutional neural network based object detector for X-Ray baggage security imagery[C]. Proceedings of 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), IEEE, 2019: 1757-1761. |
[12] |
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, 2018, 13(9): 2203-2215. doi: 10.1109/TIFS.2018.2812196 |
[13] |
ZHU Y, ZHANG H G, AN J Y, et al. GAN-based data augmentation of prohibited item X-ray images in security inspection[J]. Optoelectronics letters, 2020, 16(3): 225-229. |
[14] |
AKÇAY S, ATAPOUR-ABARGHOUEI A, BRECKON T P. Skip-GANomaly: skip connected and adversarially trained encoder-decoder anomaly detection[C]. Proceedings of 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, 2019. |
[15] |
AYDIN I, KARAKOSE M, AKIN E. A new approach for baggage inspection by using deep convolutional neural networks[C]. Proceedings of 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), IEEE, 2018: 1-6. |
[16] |
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, 2017, 47(4): 682-692. doi: 10.1109/TSMC.2016.2628381 |
[17] |
GALVEZ R L, DADIOS E P, BANDALA A A, et al.. Threat object classification in X-ray images using transfer learning[C]. Proceedings of 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), IEEE, 2018: 1-5. |
[18] |
HOWARD A G, ZHU M L, CHEN B, et al.. MobileNets: efficient convolutional neural networks for mobile vision applications[C]. Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2017. |
[19] |
IANDOLA F N, HAN S, MOSKEWICZ M W, et al.. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size[C]. Proceedings of 2017 International Conference on Learning Representations (ICLR), Toulon, France, 2017. |
[20] |
CHEN Y P, FAN H Q, XU B, et al.. Drop an octave: reducing spatial redundancy in convolutional neural networks with octave convolution[C]. Proceedings of 2019 IEEE/CVF International Conference on Computer Vision, IEEE, 2019: 3434-3443. |
[21] |
CHO K, VAN MERRIËNBOER B, GULCEHRE C, et al.. Learning phrase representations using RNN encoder-decoder for statistical machine translation[C]. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, 2014: 1724-1734. |
[22] |
董潇潇, 何小海, 吴晓红, 等. 基于注意力掩模融合的目标检测算法[J]. 液晶与显示,2019,34(8):825-833. doi: 10.3788/YJYXS20193408.0825
DONG X X, HE X H, WU X H, et al. Object detection algorithm based on attention mask fusion[J]. Chinese Journal of Liquid Crystals and Displays, 2019, 34(8): 825-833. (in Chinese) doi: 10.3788/YJYXS20193408.0825 |
[23] |
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]. Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2019: 2119-2128. |
[24] |
SZEGEDY C, VANHOUCKE V, IOFFE S, et al.. Rethinking the inception architecture for computer vision[C]. Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2016: 2818-2826. |
[25] |
SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C]. Proceedings of the 3rd International Conference on Learning Representations, 2014. |
[26] |
HE K M, ZHANG X Y, REN SH Q, et al.. Deep residual learning for image recognition[C]. Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2016. |
[27] |
HUANG G, LIU ZH, VAN DER MAATEN L, et al.. Densely connected convolutional networks[C]. Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2017. |
[28] |
WANG A L, WANG M H, JIANG K Y, et al.. A novel lidar data classification algorithm combined densenet with STN[C]. Proceedings of 2019 IEEE International Geoscience and Remote Sensing Symposium, IEEE, 2019: 2483-2486. |