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Dadashtabar Ahmadi, K., kiaei, A. (2023). Improve the detection of dangerous objects in x-ray images in security and military inspections using image processing approaches. Electronic and Cyber Defense, 10(4), 81-89.
Kourosh Dadashtabar Ahmadi; ali akbar kiaei. "Improve the detection of dangerous objects in x-ray images in security and military inspections using image processing approaches". Electronic and Cyber Defense, 10, 4, 2023, 81-89.
Dadashtabar Ahmadi, K., kiaei, A. (2023). 'Improve the detection of dangerous objects in x-ray images in security and military inspections using image processing approaches', Electronic and Cyber Defense, 10(4), pp. 81-89.
Dadashtabar Ahmadi, K., kiaei, A. Improve the detection of dangerous objects in x-ray images in security and military inspections using image processing approaches. Electronic and Cyber Defense, 2023; 10(4): 81-89.

Improve the detection of dangerous objects in x-ray images in security and military inspections using image processing approaches

پدافند الکترونیکی و سایبری
Article 9, Volume 10, Issue 4 - Serial Number 40, January 2023, Pages 81-89    PDF (1.01 M)
Document Type: Original Article
Authors
Kourosh Dadashtabar Ahmadi* ; ali akbar kiaei
Assistant Professor, Department of Artificial Intelligence and Robotics, Faculty of Electrical and Computer Engineering, Malek Ashtar University of Technology, Tehran, Iran
Receive Date: 21 February 2022,  Revise Date: 27 April 2022,  Accept Date: 09 August 2022 
Abstract
Detection of dangerous objects in images obtained by X-ray scanners in security inspections has played an important role in protecting the public space from security threats such as terrorism and the occurrence of dangerous crimes. Perform diagnostic operations by an expert despite the remarkable features of the human sensory and visual systems; Due to being exhausting, non-stop, excessive dependence on human error, etc., it has low operational value. One suitable solution for similar situations is to use car vision systems. In this study, we intend to first examine the hazardous object in the x-ray images in the SIX-ray database in a training phase with hard segmentation, and by extracting the properties of these objects by the SURF algorithm, which is capable of extracting properties even in complex conditions. It is confusing to create a database of properties of objects in different dimensions and angles. Then, in the detection phase, the experimental image first goes through a soft segmentation step, and then the image properties are extracted by the SURF algorithm. The extracted properties are matched with the properties of the object in the training database, and then the probability of the object being present, which is the ratio of the number of matching properties of the object to the total number of properties in the object, is calculated for each case. be. After finding the most likely valid matches, the M-estimator sample consensus algorithm (MSAC) removes the incorrect matching properties that originated from the image background. Finally, a two-dimensional transfer (Affine transformation) is obtained between the pairs of matching points of each valid state with the input image, and with the help of this transfer and dimensionality, a square is drawn around the object and the location of the object is identified. The following is a complete description of the training and diagnosis phase and the results of SIX-ray data.
Keywords
X-ray images; image processing; SURF algorithm; dangerous object detection
References

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