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Unmanned aerial vehicles supporting imagery intelligence using the structured light technology

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
One of the possible tasks for unmanned aerial vehicles (UAVs) is field capturing of object images. The field capturing of object images (scenes) is possible owing to the UAV equipped with photographic cameras, TV cameras, infrared cameras or synthetic aperture radars (SAR). The result of the recognition is a metric mapping of space, i.e. 2D flat images. In order to increase the quality of image recognition, it is necessary to search for and develop stereoscopic visualization with the possibility of its mobile use. A pioneering approach presented in the research paper is using a UAV with an imagery intelligence system based on structured light technology for air reconnaissance of object over a selected area or in a given direction in the field. The outcome of imagery intelligence is a three-dimensional (3D imaging) information on the geometry of an observed scene. The visualization with a stereoscopic interface proposed in the work allows for a natural perception of the depth of the scene and mutual spatial relationships, as well as seeing which objects are closer and which are further. The essence of the article is to present the application of three-dimensional vision measurement technology on UAVs. The paper presents an analysis of the possibilities of using UAVs for image recognition and a method of image recognition based on the technology of structural lighting using the method of projection of Gray’a fringes and codes. The designed image recognition system based on the structural lighting technology is described. It also discusses task modules forming a measuring head, i.e., projection, detection and calculation modules, and the exchange of control or measurement data between imaging system components. It presents the results of tests on the possibility of rapidly acquiring images using a UAV. The test results and the analyses indicate that using a UAV with an imaging technology based on structural light can contribute to improving the abilities to detect, identify, locate and monitor objects at close range, within a selected direction outdoors or indoors.
Rocznik
Strony
35--45
Opis fizyczny
Bibliogr. 19 poz., fot., rys.
Twórcy
  • Air Force Institute of Technology, Warsaw, Poland
  • Air Force Institute of Technology, Warsaw, Poland
Bibliografia
  • [1] ADAM, A., DANN, C., YAIR, O., Mazor, S., & NOWOZIN, S., 2017. Bayesian Time-of-Flight for Realtime Shape. Illumination and Albedo. IEEE T-PAMI 39 (5): 851–864.
  • [2] AGUILAR, W.G., LUNA, M.A., MOYA, J.F., ABAD, V., RUIZ, H., PARRA, H., & ANGULO, C., 2017. Pedestrian detection for UAVs using cascade classifiers and saliency maps. In International Work-Conference on Artificial Neural Networks; Càdiz, Spain, Springer. 563–574.
  • [3] BASSAN S., 2018. Empirical modeling of the relationship between decision sight distance and stopping sight distance based on AASHTO. Archives of Transport, 48 (4), 7-25.
  • [4] GUPTA, M., YIN Q., & NAYAR, S., 2013. Structured Light in Sunlight. In IEEE International Conference on Computer Vision. 545-552.
  • [5] HILDMANN, H., & Kovacs, E., 2019. Using Unmanned Aerial Vehicles (UAVs) as Mobile Sensing Platforms (MSPs) for Disaster Response, Civil Security and Public Safety. Drones, 3 (59), 1-26.
  • [6] JACYNA, M., WASIAK, M., & BOBIŃSKI, A., 2017. SIMMAG3D as a tool for designing of storage facilities in 3D. Archives of Transport, 42 (2), 25-38.
  • [7] KAMPF, R., HANZL, J., STOPKA, O., RYBICKA, I. Possibilities of using unmanned aerial vehicles for biological protection of airports in Europe. Scientific Journal of Silesian University of Technology. Series Transport, 2019, 104, 47-56.
  • [8] KE, R., LI, Z., TANG, J., PAN, Z., & WANG, Y., 2018. Real-time traffic flow parameter estimation from UAV video based on ensemble classifier and optical flow. IEEE Trans. Intell. Transp. Syst., 20, 54–64.
  • [9] KRASSANAKIS, V., PERREIRA DA SILVA, M., & RICORDEL, V., 2018. Monitoring Human Visual Behavior during the Observation of Unmanned Aerial Vehicles (UAVs) Videos. Drones, 2, 36.
  • [10] LIU, K., WANG, Y., LAU, D.L., HAO, Q., & HASSEBROOK L.G., 2010. Dual-frequency pattern scheme for high-speed 3-D shape measurement. Optics Express, 18, 5229-5244.
  • [11] ŁUKASIEWICZ, J., 2020. Unmanned aerial vehicle as a device supporting the physical protection system of critical infrastructure facilities: nuclear power plant as a case in point. Scientific Journal of Silesian University of Technology. Series Transport, 108, 121-131.
  • [12] MORANDUZZO, T., & MELGANI, F., 2014. Detecting Cars in UAV Images With a Catalog-Based Approach. Remote Sensing, 52, 6356–6367.
  • [13] SALVI, J., FERNANDEZ, S., PRIBANIC, T., & LLADO X., 2010. A state of the art in structured light patterns for surface profilometry. Pattern Recognition, 43(8).
  • [14] WANG, Y., LIU, K., LAU, D., HAO, Q., & Hassebrook, L., 2010. Maximum SNR pattern strategy for phase shifting methods in structured light illumination. JOSA A, 27(9), 1962-1971.
  • [15] WATTS, A.C., AMBROSIA, V.G., & HINKLEY, E.A., 2012. Unmanned Aircraft Systems in Remote Sensing and Scientific Research: Classification and Considerations of Use. Remote Sensing, 4(6), 1671-1692.
  • [16] WEINGARTEN, J., GRUENER, G., & SIEGWART, R., 2004. Probabilistic Plane Fitting in 3D and an Application to Robotic Mapping. Proceedings of ICRA, New Orleans.
  • [17] ZHU, J.S., SUN, K., JIA, S., LI, Q.Q., HOU, X.X., LIN, W.D., LIU, B.Z., & QIU, G.P., 2018. Urban Traffic Density Estimation Based on Ultrahigh-Resolution UAV Video and Deep Neural Network. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sensing, 12, 4968-4981.
  • [18] ZHAO, Y., MA, J., LI, X., & ZHANG, J., 2018. Saliency detection and deep learning-based wildfire identification in UAV imagery. Sensors, 18, 712.
  • [19] ZHOU, Y., TANG, D., ZHOU, H., XIANG, X., & HU, T., 2019. Vision-Based Online Localization and Trajectory Smoothing for Fixed-Wing UAV Tracking a Moving Target. In Proceedings of the IEEE International Conference on Computer Vision Workshops, Seoul, Korea.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8dff13bf-1b22-42d3-a564-7903e1a4ae4c
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