Eurasian Soil Science.
Vol. 55.
2022.
P. 1546-1555
Using Neural Networks to Detect Anomalies in X-Ray Images Obtained with Full-Body Scanners
In this paper, we solve the problem of detecting anomalies in X-ray images obtained by full-body scanners (FBSs). The paper describes the sequence and description of image preprocessing methods used to convert the original images obtained with an FBS to images with visually distinguishable anomalies. Examples of processed images are given. The first (preliminary) results of using a neural network for anomaly detection are shown.
Authors
Markov A.S.
1
,
Kotlyarov E.Y.
1
,
Anosova N.P.
1
,
Popov V.A.
1
,
Karandashev Y.M.
1,
2
,
Apushkinskaya D.E.
1
Journal
Publisher
Maik Nauka Publishing / Springer SBM
Number of issue
10
Language
English
Pages
1507-1516
Status
Published
Volume
83
Year
2022
Organizations
- 1 RUDN University
- 2 Institute for Systems Analysis, Russian Academy of Sciences
Keywords
full-body scanner; x-ray image; anomaly detection; image histogram equalization; neural network; U-2-Net; Calculus of Variations and Optimal Control; Optimization; Systems Theory; control; robotics; mechatronics; mechanical engineering; Computer-Aided Engineering (CAD; CAE) and Design
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Other records
International Journal of Advanced Research.
Vol. 10.
2022.
P. 1093-1098