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Dangerous Objects Detection of X-Ray Images Using Convolution Neural Network

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Security with Intelligent Computing and Big-data Services (SICBS 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 895))

Abstract

Object detection is a popular research direction in the field of traditional visual research. It has a wide range of applications in our real life. Because the deep learning model training for object detection requires a large amount of training data, the research on object detection until now is mostly based on natural images datasets. While natural image object recognition techniques are already very mature, the research on X-ray images is still in its infancy. This paper applies X-ray image dataset for object detection experiments and in particular aims to build an X-ray security system which can be widely used in airports, subway stations and high security conference venues. Our experiments mainly focus on three kinds of dangerous objects: scissor, knife and bottle. The results show that we achieve mean average precision of 86.41% and recall rate of 87.70%.

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Acknowledgements

We thank Yokoshima Shin and Nikaido Yoji in T & S Co., Ltd., for providing us with portable X-ray device information and data collection environments.

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Authors and Affiliations

  1. Department of Information & Communication Engineering, The University of Tokyo, Tokyo, Japan

    Lekang Zou, Tanaka Yusuke & Iba Hitoshi

Authors
  1. Lekang Zou
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  2. Tanaka Yusuke
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  3. Iba Hitoshi
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Corresponding author

Correspondence to Lekang Zou .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan

    Ching-Nung Yang

  2. Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan

    Sheng-Lung Peng

  3. University of Canberra, Canberra, ACT, Australia

    Lakhmi C. Jain

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Zou, L., Yusuke, T., Hitoshi, I. (2020). Dangerous Objects Detection of X-Ray Images Using Convolution Neural Network. In: Yang, CN., Peng, SL., Jain, L. (eds) Security with Intelligent Computing and Big-data Services. SICBS 2018. Advances in Intelligent Systems and Computing, vol 895. Springer, Cham. https://doi.org/10.1007/978-3-030-16946-6_59

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