Automatic detection of prohibited items with small size in X-ray images
CSTR:
Author:
Affiliation:

1. Tianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China;2.Shenzhen Polytechnic, Shenzhen 518055, China

  • Article
  • | |
  • Metrics
  • |
  • Reference [11]
  • |
  • Related [20]
  • | | |
  • Comments
    Abstract:

    In this paper, we focus on the detection of prohibited items with small size, and establish an automatic detection model based on feature fusion single shot multibox detector (FSSD) architecture. Two modifications are carried out to improve the detection accuracy. Firstly, the semantic enrichment module (SEM) with dilated convolution is applied to extract the low level feature with strong semantic information. Secondly, a residual module (Res) with residual blocks is added in the multibox detection architecture in order to extract more adequate features for target detection. The simulation results have demonstrated a better performance of the proposed detection model for prohibited items with small size compared with the state-of-the-arts.

    Reference
    [1] Wang X, Peng Y, Lu L, Lu Z, Bagheri M and Summers R M, ChestX-ray8:Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases, IEEE Conference on Computer Vision and Pattern Recognition, 2097 (2017).
    [2] Zou Z, Shi Z, Guo Y and Ye J, Object Detection in 20 Years:A Survey, arXiv:1905.05055v2, (2019).
    [3] Li Z and Zhou F, FSSD:Feature Fusion Single Shot Multibox Detector, arXiv:1712.00960, (2017).
    [4] Mery D, Svec E and Arias M, Object Recognition in Baggage Inspection Using Adaptive Sparse Representations of X-ray Images, Image and Video Technology, 709 (2015).
    [5] Roomi M and Rajashankarii R, International Journal of Computer Science, Engineering and Information Technology 2, 187 (2012).
    [6] Turcsany D, Mouton A and Breckon T P, Improving Feature-based Object Recognition for X-ray Baggage Security Screening using Primed Visual Words, IEEE International Conference on Industrial Technology, 1140 (2013).
    [7] Wang M, Chen J, Gao F and Zhao J, Optoelectronics Letters 14, 67 (2018).
    [8] Joseph Redmon and Ali Farhadi, YOLO9000:Better, Faster, Stronger, Computer Vision and Pattern Recognition, 7263 (2016).
    [9] Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu and Alexander C, Berg and:SSD:Single Shot MultiBox Detector, European Conference on Computer Vision, 21 (2015).
    [10] Fu C Y, Liu W, and Ranga A, Tyagi A and Berg A C, DSSD :Deconvolutional Single Shot Detector, arXiv:1701.06659, (2017).
    [11] He K, Zhang X, Ren S and Sun J, Deep Residual Learning for Image Recognition, IEEE Conference on Computer Vision and Pattern Recognition, 770 (2015).
    Cited by
    Comments
    Comments
    分享到微博
    Submit
Get Citation

ZHANG Yu-tao, ZHANG Hai-gang, ZHAO Teng-fei, YANG Jin-feng. Automatic detection of prohibited items with small size in X-ray images[J]. Optoelectronics Letters,2020,16(4):313-317

Copy
Share
Article Metrics
  • Abstract:909
  • PDF: 0
  • HTML: 0
  • Cited by: 0
History
  • Received:July 18,2019
  • Revised:September 03,2019
  • Online: July 03,2020
Article QR Code