Abstract
The rapid development of unmanned aerial vehicles (UAVs) has contributed to a proliferation of multispectral aerial survey technologies and services. Aerial reconnaissance allows to obtain the detailed digital map of the area, including the geographical distribution of radiant temperatures. These temperature maps allow to find out the composition of the objects (figure out type of the material), their real dimensions and size. There are studies concerning processing of multispectral aerial survey images (both obtained in visible and infrared ranges) and selecting the UAV optimal flight altitude for detection, recognition, and identification of monitoring objects. However, the issue of developing an integrated algorithm of UAV multispectral aerial survey for classifying monitoring objects (taking into account the choice of optimal flight altitude and camera’s resolution parameters) still remains open. This article considers the choice of the flight altitude of the UAVs based on Johnson’s criteria for detection, recognition, and identification of monitoring objects. The integrated approach for conducting aerial survey based on mathematical relationship between optimal flight altitude of the UAVs and resolving power of its onboard camera is proposed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Meshcheryakov, R.V., et al.: An application of swarm of quadcopters for searching operations. IFAC-PapersOnLine 52(25), 14–18 (2019)
Ischuk, I.N., Filimonov, A.M., Dolgov, A.A., Stepanov, E.A., Tyapkin, V.N.: Algorithm for the joint processing of multispectral image data by aerial shooting with drones. Ind. Autom. Control Syst. Controll. 27–34 (2018)
Tishcenko, A.I., Ischuk, I.N., Gromov, Y.Y.: Determination of the flight altitude unmanned aerial apparatus for performing video surveil lance is required the degree of detail of information. Inf. Sensor Syst, Thermophys. Res. 2, 190–193 (2018)
Deng, H., Sun, X., Liu, M., Ye, C., Zhou, X.: Infrared small-target detection using multiscale gray difference weighted image entropy. IEEE Trans. Aerosp. Electron. Syst. 52(1), 60–72 (2016)
Silva, H., et al.: UAV trials for multi-spectral imaging target detection and recognition in maritime environment. In: OCEANS MTS/IEEE Monterey, pp. 1–6 (2016)
Wang, X., Zhang, K., Yan, J., Yin, J.: Analysis and fusion algorithm of weak target based on infrared dual-band. In: IEEE 4th International Conference on Computer and Communications (ICCC), pp. 1730–1735 (2018)
Wang, P., Wang, W., Wang, H.: Infrared unmanned aerial vehicle targets detection based on multi-scale filtering and feature fusion. In: 3rd IEEE International Conference on Computer and Communications (ICCC), pp. 1746–1750 (2017)
Lahouli, I., Chtourou, Z., Haelterman, R., De Cubber, G., Attia, R.: A fast and robust approach for human detection in thermal imagery for surveillance using UAVs. In: 15th International Multi-Conference on Systems, Signals & Devices (SSD), pp. 184–189 (2018)
Shetty, A., Disha, B.: Detection and tracking of a human using the infrared thermopile array sensor—“Grid-EYE”. In: 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), pp. 1490–1495 (2017)
Setjo, C., Achmad, B.: Thermal image human detection using Haar-cascade classifier. In: 7th International Annual Engineering Seminar (InAES), pp. 1–66 (2017)
Tan T., Teoh, S., Fow, E., Yen, K.: Embedded human detection system based on thermal and infrared sensors for anti-poaching application. In: IEEE Conference on Systems, Process and Control (ICSPC), pp. 37–42. Bandar Hilir (2016)
Maxwell, J.C.: A treatise on electricity and magnetism, 2nd edn, pp. 68–73. Oxford, Clarendon (1892)
Romanova, M.A., et al.: Simulation of thermal fields in an anisotropic alternating saturated porous medium for environmental monitoring tasks using UAV. In: Proceedings of the 12th International Conference “Management of Large-Scale System Development” (MLSD), pp. 1–3. IEEE (2019)
Ishchuk, I.N., Tyapkin, V.N., Dolgov, A.A., Bebenin, A.A.: Method of classification of technogenic objects on the basis of construction of multilayer thermal tomograms. Inf. Sensor Syst. Thermophys. Res. 1, 251–256 (2018)
Gromov, Y.Y., Ishchuk, I.N., Alekseev, V.V., Didrikh, V.E., Tyutyunnik, V.M.: Information support of finding a solution the problem of hidden objects. J. Theoret. Appl. Inf. Technol. 95(3), 615–620 (2017)
Xaud, M.F.S.; Leite, A.C.; From, P.J.: Thermal image based navigation system for skid-steering mobile robots in sugarcane crops. In: International Conference on Robotics and Automation (ICRA), pp. 1808–1814 (2019)
Acknowledgements
This research is supported by the RFBR Project No. 19-29-06044.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Trefilov, P., Mamchenko, M., Romanova, M., Ischuk, I. (2021). Improving Methods of Objects Detection Using Infrared Sensors Onboard the UAV. In: Ronzhin, A., Shishlakov, V. (eds) Proceedings of 15th International Conference on Electromechanics and Robotics "Zavalishin's Readings". Smart Innovation, Systems and Technologies, vol 187. Springer, Singapore. https://doi.org/10.1007/978-981-15-5580-0_8
Download citation
DOI: https://doi.org/10.1007/978-981-15-5580-0_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5579-4
Online ISBN: 978-981-15-5580-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)