WU Lianquan, CHU Xianteng, YANG Haitao, NIU Jinlin, HAN Hong, WANG Huapeng. X-ray Detection of Prohibited Items Based on Improved YOLOX[J]. Infrared Technology , 2023, 45(4): 427-435.
Citation: WU Lianquan, CHU Xianteng, YANG Haitao, NIU Jinlin, HAN Hong, WANG Huapeng. X-ray Detection of Prohibited Items Based on Improved YOLOX[J]. Infrared Technology , 2023, 45(4): 427-435.

X-ray Detection of Prohibited Items Based on Improved YOLOX

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  • Received Date: March 20, 2022
  • Revised Date: April 20, 2022
  • In the process of security inspection, rapid and accurate identification of prohibited items is conducive to maintaining public security. To address the problems of stack deformation, complex background interference, and small-sized contraband detection in X-ray luggage images, an improved model for contraband detection is proposed. This improvement is based on the YOLOX model. First, an attention mechanism was introduced into the backbone network to enhance the ability of the neural network to perceive contrabands. Second, in the neck part, the multi-scale feature fusion method was improved upon, and a bottom-up structure was added after the feature pyramid structure to enhance the performance ability of the network for details, thereby improving the recognition rate of small targets. Finally, the calculation method based on IOU loss was upgraded in view of the disadvantages of the loss function calculation. The weights of various loss functions were also increased according to the characteristics of the contraband detection task, and the punishment of network misjudgment was increased to optimize the model. Upon using the improved model on the SiXray dataset, an mAP of 89.72% was attained and a fast and effective FPS arrival rate of 111.7 frame/s was achieved. Compared with mainstream models, the accuracy and detection speed of the proposed model were improved.
  • [1]
    陈冰. 基于多能X射线成像的违禁物品自动识别[D]. 北京: 北京理工大学, 2018.

    CHEN B. Automatic Recognition of Prohibited Items Based on Multi-energy X-ray Imaging[D]. Beijing: Beijing Institute of Technology, 2018.
    [2]
    邰仁忠. X射线物理学[J]. 物理, 2021, 50(8): 501-511. https://www.cnki.com.cn/Article/CJFDTOTAL-WLZZ202108003.htm

    TAI R Z. X-ray physics[J]. Physics, 2021, 50(8): 501-511. https://www.cnki.com.cn/Article/CJFDTOTAL-WLZZ202108003.htm
    [3]
    McCarley J S, Kramer A F, Wickens C D, et al. Visual skills in airport-security screening[J]. Psychological Science, 2004, 15(5): 302-306. DOI: 10.1111/j.0956-7976.2004.00673.x
    [4]
    梁添汾, 张南峰, 张艳喜, 等. 违禁品X光图像检测技术应用研究进展综述[J]. 计算机工程与应用, 2021, 57(16): 74-82. DOI: 10.3778/j.issn.1002-8331.2103-0476

    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]. Computer Engineering and Applications, 2021, 57(16): 74-82. DOI: 10.3778/j.issn.1002-8331.2103-0476
    [5]
    Mery D, Mondragon G, Riffo V, et al. Detection of regular objects in baggage using multiple X-ray views[J]. Insight-Non-Destructive Testing and Condition Monitoring, 2013, 55(1): 16-20. DOI: 10.1784/insi.2012.55.1.16
    [6]
    Michel S, Mendes M, de Ruiter J C, et al. Increasing X-ray image interpretation competency of cargo security screeners[J]. International Journal of Industrial Ergonomics, 2014, 44(4): 551-560. DOI: 10.1016/j.ergon.2014.03.007
    [7]
    韩萍, 刘则徐, 何炜琨. 一种有效的机场安检X光手提行李图像两级增强方法[J]. 光电工程, 2011, 38(7): 99-105. https://www.cnki.com.cn/Article/CJFDTOTAL-GDGC201107023.htm

    HAN P, LIU Z X, HE W K, An efficient two-stage enhancement algorithm of X-ray carry-on luggage images[J]. Opto-Electronic Engineering, 2011, 38(7): 99-105. https://www.cnki.com.cn/Article/CJFDTOTAL-GDGC201107023.htm
    [8]
    Khan S U, Chai W Y, See C S, et al. X-ray image enhancement using a boundary division wiener filter and wavelet-based image fusion approach[J]. Journal of Information Processing Systems, 2016, 12(1): 35-45.
    [9]
    ZHAO B, Wolter S, Greenberg J A. Application of machine learning to x-ray diffraction-based classification[C]//Anomaly Detection and Imaging with X-Rays(ADIX) Ⅲ. International Society for Optics and Photonics, 2018, 10632: 1063205.
    [10]
    Gaus Y F A, Bhowmik N, Breckon T P. On the use of deep learning for the detection of firearms in x-ray baggage security imagery[C]//2019 IEEE International Symposium on Technologies for Homeland Security (HST), 2019: 1-7.
    [11]
    Franzel T, Schmidt U, Roth S. Object detection in multi-view X-ray images[C]//Joint DAGM (German Association for Pattern Recognition) and OAGM Symposium, 2012: 144-154.
    [12]
    王宇, 邹文辉, 杨晓敏, 等. 基于计算机视觉的X射线图像异物分类研究[J]. 液晶与显示, 2017, 32(4): 287-293. https://www.cnki.com.cn/Article/CJFDTOTAL-YJYS201704008.htm

    WANG Y, ZOU W H, YANG X M, et al. X-ray image illegal object classification based on computer vision[J]. Chinese Journal of Liquid Crystals and Displays, 2017, 32(4): 287-293. https://www.cnki.com.cn/Article/CJFDTOTAL-YJYS201704008.htm
    [13]
    Alom M Z, Taha T M, Yakopcic C, et al. The history began from alexnet: a comprehensive survey on deep learning approaches[J/OL]. arXiv preprint arXiv: 1803.01164, 2018.
    [14]
    WANG L, GUO S, HUANG W, et al. Places205-vggnet models for scene recognition[J/OL]. arXiv preprint arXiv: 1508.01667, 2015.
    [15]
    Ballester P, Araujo R M. On the performance of GoogLeNet and AlexNet applied to sketches[C]//Thirtieth AAAI Conference on Artificial Intelligence, 2016, 30(1): doi: https://doi.org/10.1609/aaai.v30i1.10171.
    [16]
    Haque M F, Lim H Y, Kang D S. Object detection based on VGG with ResNet network[C]//2019 International Conference on Electronics, Information, and Communication (ICEIC) of IEEE, 2019: 1-3(doi: 10.23919/ELINFOCOM.2019.8706476).
    [17]
    ZOU Z, SHI Z, GUO Y, et al. Object detection in 20 years: a survey[J/OL]. arXiv preprint arXiv: 1905.05055, 2019.
    [18]
    CHEN C, LIU M Y, Tuzel O, et al. R-CNN for small object detection[C]//Asian Conference on Computer Vision, 2016: 214-230.
    [19]
    Girshick R. Fast r-cnn[C]//Proceedings of the IEEE International Conference on Computer Vision, 2015: 1440-1448.
    [20]
    R Ren S, He K, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6): 1137-1149.
    [21]
    ZHANG Y, KONG W, LI D, et al. On using XMC R-CNN model for contraband detection within X-ray baggage security images[J]. Mathematical Problems in Engineering, 2020, 2020: 1-14.
    [22]
    Sigman J B, Spell G P, LIANG K J, et al. Background adaptive faster R-CNN for semi-supervised convolutional object detection of threats in x-ray images[C]//Anomaly Detection and Imaging with X-Rays (ADIX) V, 2020, 11404: 1140404.
    [23]
    Papageorgiou C P, Oren M, Poggio T. A general framework for object detection[C]//Sixth International Conference on Computer Vision (IEEE Cat. No. 98CH36271) of IEEE, 1998: 555-562.
    [24]
    LIU Z, LI J, SHU Y, et al. Detection and recognition of security detection object based on YOLO9000[C]//2018 5th International Conference on Systems and Informatics (ICSAI)of IEEE, 2018: 278-282.
    [25]
    Galvez R L, Dadios E P, Bandala A A, et al. YOLO-based Threat Object Detection in X-ray Images[C]//2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), 2019: 1-5.
    [26]
    郭守向, 张良. Yolo-C: 基于单阶段网络的X光图像违禁品检测[J]. 激光与光电子学进展, 2021, 58(8): 0810003. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ202108007.htm

    GUO S X, ZHANG L. Yolo-C: one-stage network for prohibited items detection within X-ray images[J]. Laser & Optoelectronics Progress, 2021, 58(8): 0810003. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ202108007.htm
    [27]
    穆思奇, 林进健, 汪海泉, 等. 基于改进YOLOv4的X射线图像违禁品检测算法[J]. 兵工学报, 2021, 42(12): 2675-2683. https://www.cnki.com.cn/Article/CJFDTOTAL-BIGO202112015.htm

    MU S Q, LIN J J, WANG H Q, et al. An algorithm for detection of prohibited items in X-ray images based on improved YOLOv4[J]. Acta Armamentarii, 2021, 42(12): 2675-2683. https://www.cnki.com.cn/Article/CJFDTOTAL-BIGO202112015.htm
    [28]
    董乙杉, 李兆鑫, 郭靖圆, 等. 一种改进YOLOv5的X光违禁品检测模型[J/OL]. 激光与光电子学进展, [2022-02-21]. http://kns.cnki.net/kcms/detail/31.1690.TN.20220217.1141.008.html.

    DONG Y S, LI Z X, GU J Y, et al. An improved YOLOv5 model for X-ray prohibited items detection[J]. Laser & Optoelectronics Progress: [2022-02-21]. http://kns.cnki.net/kcms/detail/31.1690.TN.20220217.1141.008.html.
    [29]
    GE Z, LIU S, WANG F, et al. YOLOX: Exceeding Yolo series in 2021[J/OL]. arXiv preprint arXiv: 2107.08430, 2021.
    [30]
    WANG C Y, LIAO H, WU Y H, et al. CSPNet: a new backbone that can enhance learning capability of CNN[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020: 390-391.
    [31]
    Woo S, Park J, Lee J Y, et al. Cbam: convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018: 3-19.
    [32]
    MIAO C, XIE L, WAN F, et al. SiXray: a large-scale security inspection x-ray benchmark for prohibited item discovery in overlapping images[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 2119-2128.
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