Remote Visual Information System for Identification of Dangerous Substances Using Unmanned Aircrafts

Article Preview

Abstract:

Development of a functional model of the process of creating a knowledge base on the recognition of objects and actions of the enemy on the basis of neural networks and fuzzy logic. The aim of the work is to develop a set of software and hardware designed for remote identification of hazardous substances by machine visual recognition of information signs of dangerous goods with the output of relevant information to the means of visual display (interface). Recommendations concerning providing UAVs with the necessary technical means to monitor the zone of emergencies are analyzed. The recommendations of the organization of radio communication between the UAV and the operator depending on the range of the UAV departure, terrain conditions etc are analyzed and given. The structural scheme of the complex of remote recognition of HC in the form of blocks, units and software and hardware is developed. As a result of the analysis of programming systems, it was found that Python programming language is the best choice to ensure the full operation of the software due to the built-in capabilities and the involvement of third-party frameworks. A database containing information on more than 3.000 HCs with detailed recommendations for emergency response is developed. The hardware and software complex for remote identification of dangerous substances by machine visual recognition of information signs of dangerous goods by UAV, consisting of unmanned aerial platform with photo-video recording means, data transmission system to ground control station, PC for processing results and related software are substantiated and developed. A test of the UAV's capabilities in recognizing danger signs with UN numbers in different lighting conditions was tested. In all cases, the HC was accurately identified. The ideas and methods proposed in this article will allow to create cheap and simple tools for rescue units of Ukraine, which deal with the consequences of emergencies related to the leakage of HCs.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

41-49

Citation:

Online since:

July 2022

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2022 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] N.A. Koroliuk, S.N. Ieremenko, Intelligent Decision Support System for Controlling Unmanned Aerial Vehicles at the Ground Control Station, Systemy obrobky informatsyi. 8 (133) (2015) 31–36 [in Russian].

Google Scholar

[2] A.S. Buryi, M.A. Shevkunov, Approach to the construction of decision support systems for the control of unmanned aerial vehicles, Transportnoe delo Rossii. 6 (2015) 22–26 [in Russian].

Google Scholar

[3] A.O. Bychenko, V.M. Nuianzin, A.I. Berezovskyi, M.O. Pustovit, The problem of identifying hazardous substances in emergencies, Pozhezhna bezpeka. 14 (2013) 38–43 [in Ukrainian].

Google Scholar

[4] Dangerous Cargo is. Marking: DSTU 4500-5:2005. – [Valid from 2005-12-28]. – Kyiv: Derzhstandart Ukraine [in Ukrainian].

Google Scholar

[5] H.A. Leshchenko, Ya.S. Mandryk, V.M. Stratonov, S.A. Davydov, Methods of using unmanned aerial vehicles during air search and rescue, Naukoiemni tekhnolohii. № 3(51) (2021) 271–280. https://doi.org/10.18372/2310-5461.51.15998 [in Ukrainian].

Google Scholar

[6] S.I. Matorin Analysis and modeling of business systems: systemological object-oriented technology. Kharkiv: KhNURE, 2002 [in Russian].

Google Scholar

[7] A.I. Timochko, S.A. Olizarenko, O.Yu. Lavrov, A method for deciphering aerial photographs based on feature space, Systemy obrobky informatsyi. 1 (126) (2015) 84–87 [in Russian].

Google Scholar

[8] S.A. Olizarenko, Development of a functional model of the process of creating a knowledge base on the recognition of objects and actions of the enemy on the basis of neural networks and fuzzy logic, Zbirnyk naukovykh prats Kharkivskoho natsionalnoho universytetu Povitrianykh Syl. 1 (50) (2017) 58–62 [in Ukrainian].

Google Scholar

[9] A. Parra, B. Zhao, A. Haddad, M. Boutin and E. J. Delp, Hazardous material sign detection and recognition, 2013 IEEE International Conference on Image Processing. (2013) 2640–2644.

DOI: 10.1109/icip.2013.6738544

Google Scholar

[10] A. Sharifi, A. Zibaei, M. Rezaei, A deep learning based hazardous materials (HAZMAT) sign detection robot with restricted computational resources, Machine Learning with Applications. 6 (2021) 100–104.

DOI: 10.1016/j.mlwa.2021.100104

Google Scholar

[11] W. Benesova, M. Kottman, O. Sidla, Hazardous sign detection for safety applications in traffic monitoring, Proc. SPIE 8301, Intelligent Robots and Computer Vision XXIX: Algorithms and Techniques, 830109 (23 January 2012). https://doi.org/10.1117/12.905813.

DOI: 10.1117/12.905813

Google Scholar

[12] O. Nuianzin, O. Kulitsa, M. Pustovit, M. Udovenko, Method of Increasing the Availability of Video Information of Aerial Monitoring in the Airspace of a City. Volume 59: Modern Technologies Enabling Safe and Secure UAV Operation in Urban Airspace.

DOI: 10.3233/nicsp210009

Google Scholar