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Tytuł artykułu

Analysis of the state of the art on non-intrusive object-screening techniques

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
PL
Prezentacja state of the art w technice nieinwazyjnej inspekcji obiektu
Języki publikacji
EN
Abstrakty
EN
The paper is devoted to an analysis of the modern methods and techniques used for non-intrusive object screening. First, currently used technology and the principle of equipment operation are described. Next, the ways for improving the reliability and efficiency of the screening process and ways for its automation are indicated. Finally, a schematic of an automated screening system that uses additional sensors and implements AI-based analysis for automatic detection and distinguishing between legal, illegal and illicit items inside the object under inspection is proposed.
PL
Artykuł poświecony jest analizie nowoczesnych metod i technik stosowanych w nieinwazyjnej detekcji obiektu. Omówione zostały obecnie używane metody detekcji, a następnie wskazano na możliwości poprawy efektywności i wiarygodności tych metod poprzez wprowadzenie automatyzacji procesów automatyzacji. W końcu pokazano schemat systemu automatycznej inspekcji, w którym wykorzystano dodatkowe czujniki oraz elementy sztucznej inteligencji, pozwalające na rozróżnianie legalnych i nielegalnych rzeczy w obiekcie poddanemu inspekcji.
Rocznik
Strony
168--173
Opis fizyczny
Bibliogr. 41 poz., rys., tab.
Twórcy
  • Turiba University, Graudu Street 68, LV-1058 Riga
  • Latvia, Kremenchuk Mykhailo Ostrohradskyi National University, Pershotravneva 20, 39600, Kremenchuk, Ukraine
autor
  • Institute of Information Technology, Riga Technical University, Kalku Street 1, LV-1658 Riga, Latvia
  • School of Aerospace Engineering, Universidad Politécnica de Madrid, 28040 Madrid, Spain
  • FLOW, Engineering Mechanics, KTH Royal Institute of Technology, SE-100 44, Stockholm, Sweden
Bibliografia
  • [1]Settey T., Gnap J., Benová D., Pavlicko M., Blažeková O. The Growth of E-Commerce Due to COVID- 19 and the Need for Urban Logistics Centers Using Electric Vehicles: Bratislava Case Study. Sustainability (2021), 13, 5357. https://doi.org/10.3390/su13105357
  • [2] Pollák, F.; Konecný, M.; Šceulovs, D. Innovations in the Management of E-Commerce: Analysis of Customer Interactions during the COVID-19 Pandemic. Sustainability (2021), 13, 7986. https://doi.org/10.3390/su13147986
  • [3] Wang, X.;Wong, Y.D.; Yuen, K.F. Does COVID-19 Promote Self-Service Usage among Modern Shoppers? An Exploration of Pandemic-Driven Behavioural Changes in Self- Collection Users. Int. J. Environ. Res. Public Health (2021), 18, 8574. https://doi.org/ 10.3390/ijerph18168574
  • [4] Saif, NMA; Ruan,J.;Obrenovic ,B. Sustaining Trade during COVID-19 Pandemic: Establishing a Conceptual Model Including COVID-19 Impact. Sustainability (2021), 13, 5418. https://doi.org/10.3390/su13105418
  • [5] Serpa, Regina C. The Exceptional Becomes Everyday: Border Control, Attrition and Exclusion from Within. Social Sciences 10:329. (2021) https://doi.org/10.3390/socsci10090329
  • [6] Petracou E.V., Domazakis G.N., Papayiannis G.I., Yannacopoulos A.N. Towards a Common European Space for Asylum. Sustainability. 2018; 10(9):2961. https://doi.org/10.3390/su10092961
  • [7] Michel, S., Mendes, M., de Ruiter, J. C., Koomen, G. C. M., & Schwaninger, A. (2014). Increasing X-ray image interpretation competency of cargo security screeners. International Journal of Industrial Ergonomics, 44(4), 551–560. doi:10.1016/j.ergon.2014.03.00
  • [8] Michel, S., Koller, S. M., de Ruiter, J. C., Moerland, R., Hogervorst, M., & Schwaninger, A. Computer-Based Training Increases Efficiency in X-Ray Image Interpretation by Aviation Security Screeners. (2007), 41st Annual IEEE International Carnahan Conference on Security Technology. doi:10.1109/ccst.2007.4373490
  • [9] Halbherr, T., Schwaninger, A., Budgell, G. R., & Wales , A . Airport Security Screener Competency: A Cross- Sectional and Longitudinal Analysis. The International Journal of Aviation Psychology, 23(2), doi:10.1080/10508414.2011.5824
  • [10] von Bastian C. C., Schwaninger A. and Michel S., Do multi-view X-ray systems improve X-ray image interpretation in airport security screening?, Zeitschrift für Arbeitswissenschaft, (2008), 3, 166–173.
  • [11] Jaccard, N., Rogers, T. W., Morton, E. J., & Griffin, L. D. Detection of concealed cars in complex cargo X-ray imagery using Deep Learning. Journal of X-Ray Science and Technology, (2017), 25(3), 323–339. doi:10.3233/xst-16199
  • [12] Caldwell M., Ransley M., Rogers T., and Griffin L. Transferring X-ray based automated threat detection between scanners with different energies and resolution, in Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies, (2017), p. 104410F.
  • [13] Mery, D., Svec, E., Arias, M., Riffo, V., Saavedra, J. M., & Banerjee, S. Modern Computer Vision Techniques for X-Ray Testing in Baggage Inspection. IEEE Transactions on Systems, Man, and Cybernetics: Systems, (2017), 47(4), 682–692. doi:10.1109/tsmc.2016.2628381
  • [14] Akcay, S., Kundegorski, M. E., Willcocks, C. G. and Breckon, T. P. Using Deep Convolutional Neural Network Architectures for Object Classification and Detection Within X-Ray Baggage Security Imagery. IEEE Transactions on Information Forensics and Security, (2018), 13(9), 2203–2215. doi:10.1109/tifs.2018.2812196
  • [15] Krizhevsky, A., Sutskever, I., & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Communications of the ACM, (2017) , 60(6), 84–90. doi:10.1145/3065386
  • [16] Simonyan K. and Zisserman A. Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv: (2014), 1409.1556.
  • [17] Vinuesa, R., Azizpour, H., Leite, I. et al. The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11(1), 233. doi:10.1038/s41467-019-14108-y
  • [18] Zheng, Y. and Elmaghraby, A. A vehicle threat detection system using correlation analysis and synthesized X- ray images. Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII. (2013) doi:10.1117/12.2016646
  • [19] Wells, K., & Bradley, D. A. A review of X-ray explosives detection techniques for checked baggage. Applied Radiation and Isotopes, (2012) , 70(8), 1729–1746. doi:10.1016/j.apradiso.2012.01
  • [20] Yoo, K. E., & Choi, Y. C. Analytic hierarchy process approach for identifying relative importance of factors to improve passenger security checks at airports. Journal of Air Transport Management, (2006), 12(3), 135–142. doi:10.1016/j.jairtraman.2005.
  • [21] Rogers, T. W., Jaccard, N., Morton, E. J., & Griffin, L. D. Automated X-ray image analysis for cargo security: Critical review and future promise. Journal of X-Ray Science and Technology, (2017), 25(1), 33–56. doi:10.3233/xst-160606
  • [22] Tuszynski, J., Briggs, J. T., & Kaufhold, J. A method for automatic manifest verification of container cargo using radiography images. Journal of Transportation Security, (2013) , 6(4), 339–356. doi:10.1007/s12198-013-0121-3
  • [23] Reims, N., Schoen, T., Boehnel, M., Sukowski, F., & Firsching, M. Strategies for efficient scanning and reconstruction methods on very large objects with high-energy x-ray computed tomography. Developments in X-Ray Tomography IX, (2014), doi:10.1117/12.2062002
  • [24] Zhang, J., Zhang, L., Zhao, Z., Liu, Y., Gu, J., Li, Q., and Zhang, D. Joint Shape and Texture Based X-Ray Cargo Image Classification. 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops. (2014), doi:10.1109/cvprw.2014.48
  • [25] Chen, G., Bjorkholm, P., Fox, T. R., Wilson, Z., Bonsergent, X., McDaniel, F. D., and Doyle, B. L. X-Ray Cargo Inspection: Status and Trends. AIP Conference Proceedings, (2009). doi:10.1063/1.3120101
  • [26] Macdonald, R. D. R. Design and implementation of a dual-energy x-ray imaging system for organic material detection in an airport security application. Proc. SPIE 4301, Machine Vision Applications in Industrial Inspection IX, (2001) doi:10.1117/12.420922
  • [27] Visser, W. at al Automated Comparison of X-Ray Images for Cargo Scanning. Proceedings of the 50th IEEE International Carnahan Conference on Security Technology, Orlando U.S.A., October 24-27, (2016), 268-275. doi:10.1109/ccst.2016.7815714
  • [28] Kolokytha, S. at al. Creating a reference database of cargo inspection X-ray images using high energy CT of cargo mock-ups. 2016 IEEE International Conference on Imaging Systems and Techniques (IST). (2016), 249-254. doi:10.1109/ist.2016.7738232
  • [29] Griffin, L. D., Caldwell, M., Andrews, J. T. A., & Bohler , H . Unexpected item in the bagging area”: Anomaly detection in X-ray security images, IEEE Transactions on Information Forensics and Security, (2019), vol. 14, Iss. 6, 1539 – 1553. doi:10.1109/tifs.2018.2881700
  • [30] Pérez, J. M., Le Clainche, S., & Vega, J. M. Reconstruction of three-dimensional flow fields from two- dimensional data. Journal of Computational Physics, (2020) 407, 109239. doi:10.1016/j.jcp.2020.109239
  • [31] Bell, E., Mendez, C., Le Clainche, S., & Vega, J. M . A reduced order model to create two-dimensional flow fields from uni-dimensional data. (2019). AIAA Scitech 2019 Forum. doi:10.2514/6.2019-2361
  • [32] Le Clainche S. (2020) An Introduction to Some Methods for Soft Computing in Fluid Dynamics. In: Martínez Álvarez F., Troncoso Lora A., Sáez Muñoz J., Quintián H., Corchado E. (eds) 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019). SOCO 2019. Advances in Intelligent Systems and Computing, vol 950. Springer, Cham. https://doi.org/10.1007/978-3-030- 20055-8_53
  • [33] Higher Order Dynamic Mode Decomposition and Its Applications J.M. Vega and S. L. Le Clainche. September 2020, Book Publisher: Elsevier ISBN: 9780128197431
  • [34] Abadía-Heredia R., López-Martín M., Carro B., Arribas J.I., Pérez J.M., Le Clainche S. A predictive hybrid reduced order model based on proper orthogonal decomposition combined with deep learning architectures, Expert Systems with Applications, Volume 187, (2022), 115910, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2021.115910.
  • [35] Ahn, J. H., Lim, J. H., Park, J., Oh, E. H., Son, M., Hong, S., & Park, T. H. Screening of target-specific olfactory receptor and development of olfactory biosensor for the assessment of fungal contamination in grain. Sensors and Actuators B: Chemical, (2015) , 210, 9–16. doi:10.1016/j.snb.2014.12.060
  • [36] Full, J., Delbrück, L., Sauer, A., & Miehe, R. Market Perspectives and Future Fields of Application of Odor Detection Biosensors—A Systematic Analysis. Proceedings, (2020) , 60(1), 40. doi:10.3390/iecb2020-07029
  • [37] Yunkwang Oh, Youngmi Lee, Heath, J., & Moonil K i m . Applications of Animal Biosensors: A Review. IEEE Sensors Journal, (2015) , 15(2), 637–645. doi:10.1109/jsen.2014.2358261
  • [38] Dung, T., Oh, Y., Choi, S.-J., Kim, I.-D., Oh, M.- K., & K i m , M . Applications and Advances in Bioelectronic Noses for Odour Sensing. Sensors, (2018) , 18(2), 103. doi:10.3390/s18010103
  • [39] Mares, J. O. at al. Thermal and mechanical response of PBX 9501 under contact excitation. Journal of Applied Physics, (2013), 113(8), 084904. doi:10.1063/1.4793495
  • [40] Zrimsek, A. B., Bykov, S. V., & Asher, S. A. Deep Ultraviolet Standoff Photoacoustic Spectroscopy of Trace Explosives. Applied Spectroscopy, (2018) , 000370281879228. doi:10.1177/0003702818792289
  • [41] Bloomfield, M., Andrews, D., Loeffen, P., Tombling, C., York, T., and Matousek, P. Non- invasive identification of incoming raw pharmaceutical materials using Spatially Offset Raman Spectroscopy. Journal of Pharmaceutical and Biomedical Analysis, (2013), 76, 65–69. doi:10.1016/j.jpba.2012.11.046
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5a321f41-05ad-4e21-a347-c703ab6d2fc4
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