Deep Convolutional Neural Network Based Object Detector for X-Ray Baggage Security Imagery

By Jinyi Liu, Xiaxu Leng, Ying Liu. 2019

50 citations recorded by the source corpus.

Detection and classification of X-ray baggage security imagery are of great significance not only in daily life. However, up to now, it is still difficult for the traditional image processing methods to detect and distinguish objects which we want to detect in X-Ray baggage security imagery. Moreover, it is even more challenging to classify different types of objects. Although the current state-of-the-art detectors have achieved impressive performance on various public datasets with visible images, these detectors fail to deal with objects in X-ray images. This paper proposes an object detection algorithm for X-Ray baggage security screening images. Firstly, to outline the detected object from X-Ray baggage security imagery, we propose a fore-ground-background segmentation method which based on color information. Then, to classify and outline different type of object in X-Ray im-age, a deep convolutional neural networks (DCNNs) based object detection framework Faster R-CNN are proposed. In this stage we also use transfer learning method to speed up Faster R-CNN. The proposed method is proved to achieve 77% mAP by in a real-word dataset which contains 32,253 sub-way X-Ray baggage security screening images.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

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In Biomedical Engineering

  1. Longyin Wen

In Computer Vision and Pattern Recognition

  1. Libo Zhang
  2. Boying Wang
  3. Longyin Wen
  4. Xianglong Liu
  5. Yanjun Wu

In Radiology, Nuclear Medicine and Imaging

  1. Libo Zhang
  2. Longyin Wen
  3. Xianglong Liu
  4. Yanjun Wu

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