Keywords
x-ray security check
digital twin
automatic baggage inference
neural network
YOLO
digital twin
automatic baggage inference
neural network
YOLO
Abstract
Artificial intelligence-assisted recognition of dangerous items at baggage inspection is essential for traveler safety. In our research, we used a digital twin of a suitcase with three dangerous items placed among ordinary items: a knife, a revolver, and a grenade. We aimed to investigate the impact of granular noise on the quality of recognizing dangerous objects, depending on the adopted color model. For our research, we used the YOLOv7 neural network, which was trained on images of individual color models undistorted by noise for 300 epochs. Test sets with different noise levels were then recognized by the trained network. The confusion matrix for the trained images from the digital twin revealed high-quality recognition of individual objects. Research into the influence of grain noise on the recognition of dangerous objects revealed poor resistance of colored images on a white background to such noise. The best results for recognizing dangerous objects were recorded for black-and-white negative images, which are commonly used in medical x-rays.
References
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[2] M.F. Syahputra, R.F. Rahmat, R. Rambe, Identification of Lung Cancer on Chest X-Ray (CXR) Medical Images Using the Probabilistic Neural Network Method, J. Phys.: Conf. Ser. 1898 (2021) 012023. https://doi.org/10.1088/1742-6596/1898/1/012023.
[3] D.P. Yadav, S. Rathor, Bone Fracture Detection and Classification using Deep Learning Approach, in: 2020 International Conference on Power Electronics & IoT Applications in Renewable Energy and Its Control (PARC), 2020: pp. 282–285. https://doi.org/10.1109/PARC49193.2020.236611.
[4] A.M. Tahir, M.E.H. Chowdhury, A. Khandakar, T. Rahman, Y. Qiblawey, U. Khurshid, S. Kiranyaz, N. Ibtehaz, M.S. Rahman, S. Al-Maadeed, S. Mahmud, M. Ezeddin, K. Hameed, T. Hamid, COVID-19 infection localization and severity grading from chest X-ray images, Computers in Biology and Medicine 139 (2021) 105002. https://doi.org/10.1016/j.compbiomed.2021.105002.
[5] J. Damilakis, J.E. Adams, G. Guglielmi, T.M. Link, Radiation exposure in X-ray-based imaging techniques used in osteoporosis, Eur Radiol 20 (2010) 2707–2714. https://doi.org/10.1007/s00330-010-1845-0.
[6] T.W. Rogers, N. Jaccard, E.J. Morton, L.D. Griffin, Automated X-ray image analysis for cargo security: Critical review and future promise, Journal of X-Ray Science and Technology 25 (2017) 33–56. https://doi.org/10.3233/XST-160606.
[7] M. Rafiei, J. Raitoharju, A. Iosifidis, Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey, IEEE Access 11 (2023) 2445–2477. https://doi.org/10.1109/ACCESS.2023.3234187.
[8] S. Niewiarowski, A. Gogbashian, A. Afaq, R. Kantor, Z. Win, Abdominal X-ray signs of intra-intestinal drug smuggling, Journal of Forensic and Legal Medicine 17 (2010) 198–202. https://doi.org/10.1016/j.jflm.2009.12.013.
[9] W. Visser, A. Schwaninger, D. Hardmeier, A. Flisch, M. Costin, C. Vienne, F. Sukowski, U. Hassler, I. Dorion, A. Marciano, G. Koomen, M. Slegt, A.C. Canonica, Automated comparison of X-ray images for cargo scanning, in: 2016 IEEE International Carnahan Conference on Security Technology (ICCST), 2016: pp. 1–8. https://doi.org/10.1109/CCST.2016.7815714.
[10] G. Narayana Samy, R. Ahmad, Z. Ismail, Security threats categories in healthcare information systems, Health Informatics J 16 (2010) 201–209. https://doi.org/10.1177/1460458210377468.
[11] A. Petrozziello, I. Jordanov, Automated Deep Learning for Threat Detection in Luggage from X-Ray Images, in: I. Kotsireas, P. Pardalos, K.E. Parsopoulos, D. Souravlias, A. Tsokas (Eds.), Analysis of Experimental Algorithms, Springer International Publishing, Cham, 2019: pp. 505–512. https://doi.org/10.1007/978-3-030-34029-2_32.
[12] D. Vukadinovic, M.R. Osés, D. Anderson, Automated detection of inorganic powders in X-ray images of airport luggage, J Transp Secur 16 (2023) 3. https://doi.org/10.1007/s12198-023-00261-5.
[13] X. Yu, W. Yuan, A. Wang, X-ray Security Inspection Image Dangerous Goods Detection Algorithm Based on Improved YOLOv4, Electronics 12 (2023) 2644. https://doi.org/10.3390/electronics12122644.
[14] M. Chouai, M. Merah, J.-L. Sancho-Gómez, M. Mimi, Supervised feature learning by adversarial autoencoder approach for object classification in dual X-ray image of luggage, J Intell Manuf 31 (2020) 1101–1112. https://doi.org/10.1007/s10845-019-01498-5.
[15] J. Hiller, P. Hornberger, Measurement accuracy in X-ray computed tomography metrology: Toward a systematic analysis of interference effects in tomographic imaging, Precision Engineering 45 (2016) 18–32. https://doi.org/10.1016/j.precisioneng.2015.12.003.
[16] B. Yagoub, H. Ibrahem, A. Salem, H.-S. Kang, Single Energy X-ray Image Colorization Using Convolutional Neural Network for Material Discrimination, Electronics 11 (2022) 4101. https://doi.org/10.3390/electronics11244101.
[17] A. Dawid, P. Buchwald, B. Pawlak, The Digital Twin to Train a Neural Network Detecting Headlamps Failure of Motor Vehicles, in: W. Zamojski, J. Mazurkiewicz, J. Sugier, T. Walkowiak, J. Kacprzyk (Eds.), Dependable Computer Systems and Networks, Springer Nature Switzerland, Cham, 2023: pp. 29–38. https://doi.org/10.1007/978-3-031-37720-4_3.
[18] T. Mandl, C. Östlin, I.E. Dawod, M.N. Brodmerkel, E.G. Marklund, A.V. Martin, N. Timneanu, C. Caleman, Structural Heterogeneity in Single Particle Imaging Using X-ray Lasers, J. Phys. Chem. Lett. 11 (2020) 6077–6083. https://doi.org/10.1021/acs.jpclett.0c01144.
[19] N. Tian, H. Jiang, A. Li, D. Liang, S. Yan, Z. Zhang, Influence of diffuser grain size on the speckle tracking technique, J Synchrotron Radiat 27 (2020) 146–157. https://doi.org/10.1107/S1600577519015200.
[20] T.E. Gureyev, A. Kozlov, A.J. Morgan, A.V. Martin, H.M. Quiney, Effect of radiation damage and illumination variability on signal-to-noise ratio in X-ray free-electron laser single-particle imaging, Acta Crystallogr A Found Adv 76 (2020) 664–676. https://doi.org/10.1107/S2053273320012188.
[21] S.-J. Tu, W.-Y. Chen, C.-T. Wu, Uncertainty measurement of radiomics features against inherent quantum noise in computed tomography imaging, Eur Radiol 31 (2021) 7865–7875. https://doi.org/10.1007/s00330-021-07943-5.
[22] L. Bai, A. Velichko, B.W. Drinkwater, Grain Scattering Noise Modeling and Its Use in the Detection and Characterization of Defects Using Ultrasonic Arrays, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 66 (2019) 1798–1813. https://doi.org/10.1109/TUFFC.2019.2927439.
[23] J. Tanguay, J. Kim, H.K. Kim, K. Iniewski, I.A. Cunningham, Frequency-dependent signal and noise in spectroscopic x-ray imaging, Med Phys 47 (2020) 2881–2901. https://doi.org/10.1002/mp.14160.
[24] I.A. Artyukov, N.N. Irtuganov, Noise-Driven Anisotropic Diffusion Filtering for X-Ray Low Contrast Imaging, J Russ Laser Res 40 (2019) 150–154. https://doi.org/10.1007/s10946-019-09782-8.
[25] H. Fang, E. Hovad, Y. Zhang, L.K.H. Clemmensen, B.K. Ersbøll, D. Juul Jensen, Deep learning for improving non-destructive grain mapping in 3D, IUCrJ 8 (2021) 719–731. https://doi.org/10.1107/S2052252521005480.
[26] A. Dawid, PSR-based research of feature extraction from one-second EEG signals: a neural network study, SN Appl. Sci. 1 (2019) 1536. https://doi.org/10.1007/s42452-019-1579-9.
[27] T. Kuchkorov, J. Khamzaev, Z. Allamuratova, T. Ochilov, Traffic and road sign recognition using deep convolutional neural network, in: 2021 International Conference on Information Science and Communications Technologies (ICISCT), 2021: pp. 1–5. https://doi.org/10.1109/ICISCT52966.2021.9670228.
[28] A. Dawid, P. Buchwald, The system of headlights operation recognition using the digital twin method, International Journal of Electronics and Telecommunications Vol. 70, No. 1 (2024). https://doi.org/10.24425/ijet.2024.149514.
[29] J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You Only Look Once: Unified, Real-Time Object Detection, (2016). http://arxiv.org/abs/1506.02640 (accessed March 15, 2023).
[30] J. Terven, D. Cordova-Esparza, A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS, MAKE 5 (2023) 1680–1716. https://doi.org/10.3390/make5040083.
[31] Ultralytics, Object Detection Datasets Overview, (n.d.). https://docs.ultralytics.com/datasets/detect (accessed June 25, 2024).
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