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Improving Automated Baggage Inspection Using Simulated X-ray Images of 3D Models

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Image and Video Technology (PSIVT 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13763))

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Abstract

X-ray baggage inspection is essential to ensure transport and border security, as it prevents hazardous objects from entering secure areas. Currently, deep learning is the state-of-the-art approach for automated threat object detection and classification. Proper training of these networks requires substantial data; however, the number of publicly available datasets of X-ray images is limited. To overcome this problem, we propose a method for generating new data by superimposing simulated X-ray images of 3D models onto real baggage X-rays, allowing researchers to train deep neural networks without requiring additional imaging or manual labeling. To validate our proposal, we ran experiments using 3D models of wrenches and the SIXray baggage dataset. The results prove that superimposing synthetic threat objects over a real training subset improves detection performance, with average precision (AP) increasing from 90.2% to 93.7%. As modern object detectors process images in real-time, they prove themselves as a feasible approach for aiding inspectors and even fully automating baggage inspection. Moreover, the novel superimposition and colorization techniques presented in this study can be employed in other areas of X-ray imaging.

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Notes

  1. 1.

    Some 3D model websites are Sketchfab, Turbosquid, Thingiverse, and Printables.

  2. 2.

    Available at https://github.com/anthonytec2/xrayscanner.

  3. 3.

    Script that extracts data from a website.

  4. 4.

    Available at https://github.com/ultralytics/yolov5.

  5. 5.

    Available at https://github.com/kaminetzky/axis.

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Acknowledgments

This work was supported by National Center for Artificial Intelligence CENIA FB210017, Basal ANID, and ANID National Master’s Scholarship 2021 N\(^\circ \)22211094.

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Authors and Affiliations

  1. Department of Computer Science, Pontificia Universidad Católica de Chile, Santiago, Chile

    Alejandro Kaminetzky & Domingo Mery

Authors
  1. Alejandro Kaminetzky
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  2. Domingo Mery
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Corresponding author

Correspondence to Alejandro Kaminetzky .

Editor information

Editors and Affiliations

  1. Xiamen University Malaysia, Sepang, Malaysia

    Han Wang

  2. Singapore Institute of Manufacturing Technology, Singapore, Singapore

    Wei Lin

  3. Charles Sturt University, Bathurst, NSW, Australia

    Paul Manoranjan

  4. Minjiang University, Fuzhou, China

    Guobao Xiao

  5. Yau Lee Holdings Ltd., Hong Kong, Hong Kong

    Kap Luk Chan

  6. Tsinghua University, Beijing, China

    Xiaonan Wang

  7. Nanyang Technological University, Singapore, Singapore

    Guiju Ping

  8. Nanyang Technological University, Singapore, Singapore

    Haoge Jiang

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Kaminetzky, A., Mery, D. (2023). Improving Automated Baggage Inspection Using Simulated X-ray Images of 3D Models. In: Wang, H., et al. Image and Video Technology. PSIVT 2022. Lecture Notes in Computer Science, vol 13763. Springer, Cham. https://doi.org/10.1007/978-3-031-26431-3_10

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  • DOI: https://doi.org/10.1007/978-3-031-26431-3_10

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  • Online ISBN: 978-3-031-26431-3

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