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Dualray: Dual-View X-ray Security Inspection Benchmark and Fusion Detection Framework

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Part of the book series:Lecture Notes in Computer Science ((LNCS,volume 13537))

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Abstract

Prohibited item detection in X-ray security inspection images using computer vision technology is a challenging task in real world scenarios due to various factors, include occlusion and unfriendly imaging viewing angle. Intelligent analysis of multi-view X-ray security inspection images is a relatively direct and targeted solution. However, there is currently no published multi-view X-ray security inspection image dataset. In this paper, we construct a dual-view X-ray security inspection dataset, named Dualray, based on real acquisition method. Dualray dataset consists of 4371 pairs of images with 6 categories of prohibited items, and each pair of instances is imaged from horizontal and vertical viewing angles. We have annotated each sample with the categories of prohibited item and the location represented by bounding box. In addition, a dual-view prohibited item feature fusion and detection framework in X-ray images is proposed, where the two input channels are applied and divided into primary and secondary channels, and the features of the secondary channel are used to enhance the features of the primary channel through the feature fusion model. Spatial attention and channel attention are employed to achieve efficient feature screening. We conduct some experiments to verify the effectiveness of the proposed dual-view prohibited item detection framework in X-ray images. The Dualray dataset and dual-view object detection code are available athttps://github.com/zhg-SZPT/Dualray.

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Acknowledgments

This work was supported in part by Shenzhen Science and Technology Program (No. RCBS20200714114940262), and in part by General Higher Education Project of Guangdong Provincial Education Department (No. 2020ZDZX3082), and in part by Stable Supporting Programme for Universities of Shenzhen (No. 20200825181232001).

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Authors and Affiliations

  1. Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area, Shenzhen Polytechnic, Shenzhen, 518055, Guangdong, China

    Modi Wu, Feifan Yi, Haigang Zhang & Jinfeng Yang

  2. School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, 114045, Liaoning, China

    Modi Wu & Xinyu Ouyang

Authors
  1. Modi Wu

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  2. Feifan Yi

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  3. Haigang Zhang

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  4. Xinyu Ouyang

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  5. Jinfeng Yang

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Corresponding author

Correspondence toHaigang Zhang.

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Shiqi Yu

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhaoxiang Zhang

  3. Hong Kong Baptist University, Hong Kong, China

    Pong C. Yuen

  4. Northwestern Polytechnical University, Xi'an, China

    Junwei Han

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Hong Kong Baptist University, Hong Kong, China

    Yike Guo

  7. Sun Yat-sen University, Guangzhou, China

    Jianhuang Lai

  8. Southern University of Science and Technology, Shenzhen, China

    Jianguo Zhang

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Wu, M., Yi, F., Zhang, H., Ouyang, X., Yang, J. (2022). Dualray: Dual-View X-ray Security Inspection Benchmark and Fusion Detection Framework. In: Yu, S.,et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13537. Springer, Cham. https://doi.org/10.1007/978-3-031-18916-6_57

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