Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/5029
Title: X-ray image analysis for explosive circuit detection using deep learning algorithms
Authors: Seyfi, G.
Yilmaz, M.
Esme, E.
Kiran, M.S.
Keywords: Classification
Dangerous substance
Deep learning
X-ray image
Automation
Deep learning
Explosives
Image analysis
Image classification
Learning algorithms
Learning systems
Medical imaging
Military applications
Military photography
Timing circuits
Dangerous substances
Deep learning
Electronic cards
F measure
G-means
Image-analysis
Military facilities
ShuffleNets
X-ray image
X-ray imaging technologies
Laptop computers
Issue Date: 2024
Publisher: Elsevier Ltd
Abstract: X-ray imaging technologies find applications across various domains, including medical imaging in health institutions or security in military facilities and public institutions. X-ray images acquired from diverse sources necessitate analysis by either trained human experts or automated systems. In cases where concealed electronic cards potentially pose threats, such as in laptops harboring explosive triggering circuits, conventional analysis methods are challenging to detect, even when scrutinized by skilled. The present investigation is centered on the utilization of deep learning algorithms for the analysis of X-ray images of laptop computers, with the aim of identifying concealed hazardous components. To construct the dataset, some control cards such as Arduino, Raspberry Pi and Bluetooth circuits were hidden inside the 60 distinct laptop computers and were subjected to X-ray imaging, yielding a total of 5094 X-ray images. The primary objective of this study is to distinguish laptops based on the presence or absence of concealed electronic cards. To this end, a suite of deep learning models, including EfficientNet, DenseNet, DarkNet19, DarkNet53, Inception, MobileNet, ResNet18, ResNet50, ResNet101, ShuffleNet and Xception were subjected to training, testing, and comparative evaluation. The performance of these models was assessed utilizing a range of metrics, encompassing accuracy, sensitivity, specificity, precision, f-measure, and g-mean. Among the various models examined, the ShuffleNet model emerged as the top-performing one, yielding superior results in terms of accuracy (0.8355), sensitivity (0.8199), specificity (0.8530), precision (0.8490), f-measure (0.8322), and g-mean (0.8352). © 2023 Elsevier B.V.
URI: https://doi.org/10.1016/j.asoc.2023.111133
https://hdl.handle.net/20.500.13091/5029
ISSN: 1568-4946
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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