[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

Image Processing in Unmanned Aerial Vehicles

  • Chapter
  • First Online:
Unmanned Aerial Vehicles in Smart Cities

Part of the book series: Unmanned System Technologies ((UST))

Abstract

Even flying attempts of humans are back to centuries; modern aviation started in the eighteenth century with hot air balloons. The speed of mechanization and the developments in electronics provided rapid improvements in aviation, and in the last century, planes became the most important vehicles in the world for military, civil, and engineering usages. Drones—or unmanned aerial vehicles—are the results of wireless developments while they can be controlled remotely or autonomously. Their different sizes and endurances make them suitable for any kind of tasks that humans are not able to perform or reach. Thus, the obtained images also become important, and processing these images require different algorithms for different kinds of applications. In this chapter, UAVs are classified according to the image processing types as segmentation and analysis, identification and prediction, and 3D reconstruction and applications and example applications, and considered image processing techniques are presented for each category with their details. Also, recent classifications are extended by considering new researches and applications.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 71.50
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 89.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
GBP 89.99
Price includes VAT (United Kingdom)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. D. Kaimaris, P. Patias, M. Sifnaiou, UAV and the comparison of image processing software. Int. J. Intell. Unmanned Syst. 5(1), 18–27 (2017)

    Article  Google Scholar 

  2. A. Mairaj, A.I. Baba, A.Y. Javaid, Application specific drone simulators: recent advances and challenges. Simul. Model. Pract. Theor. 94, 100–117 (2019)

    Article  Google Scholar 

  3. L. Reich, How drones are being used in disaster managements? – Geoawesomeness. https://geoawesomeness.com/drones-fly-rescue/. Accessed 16 Nov 2019

  4. M. Altaweel, The use of drones in human and physical geology—GIS Lounge. https://www.gislounge.com/use-drones-human-physical-geography/. Accessed 15 Nov 2019

  5. M.A. Azevedo, Drones and journalism—The Network, 2017. https://newsroom.cisco.com/feature-content?articleId=1851973. Accessed 15 Nov 2019

  6. S. Long, Drones and precision agriculture: the future of farming (2017). https://www.microdrones.com/en/content/drones-and-precision-agriculture-the-future-of-farming/. Accessed 11 Nov 2019

  7. C. Snow, Why drones are the future of internet of things—sUAS news—The business of drones. https://www.suasnews.com/2014/12/why-drones-are-the-future-of-the-internet-of-things/. Accessed 14 Nov 2019

  8. https://alchetron.com/Bayraktar-Mini-UAV. Accessed 18 Nov 2019

  9. G. Barlow, Design of autonomous navigation controllers for unmanned aerial vehicles using multi-objective genetic programming–Scientific Figure on ResearchGate. https://www.researchgate.net/figure/The-Predator-medium-altitude-long-endurance-unmanned-aerial-vehicle_fig2_235188311. Accessed 18 Nov 2019

  10. G. Cai, J. Dias, L. Seneviratne, A survey of small-scale unmanned aerial vehicles: recent advances and future development trends. Unmanned Syst. 2(2), 175–199. https://www.researchgate.net/figure/Featured-small-scale-flapping-wing-UAVs_fig8_270723492. Accessed 18 Nov 2019 (2014)

    Article  Google Scholar 

  11. C. Patruno, M. Nitti, A. Petitti, E. Stella, T. D’Orazio, A vision-based approach for unmanned aerial vehicle landing. J. Intell. Robot. Syst. 95(5), 645–664 (2019)

    Article  Google Scholar 

  12. N. Saadat and M.M.M. Sharif, Unmanned aerial vehicle surveillance system (UAVSS) for forest surveillance and data acquisition, 2017 International Conference on Information and Communication Technology Convergence (ICTC), Jeju (2017), pp. 178–183

    Google Scholar 

  13. J.W. Orillo, G.B. Bernardo Jr., J.J. Dizon, C. Imperial, H. Macabenta, A.M. Macabenta, R. Palima Jr., Determination of green leaves density using normalized difference vegetation index via image processing of in-field drone-captured image. J. Telecommun. Electron. Comput. Eng. 9(2–6), 2289–8131 (2017)

    Google Scholar 

  14. L.Y. Seul, L.P. Hien, P.J. Soo, L.M. Hee, P.M. Wook, K. Jee-in, Calculation of tree height and canopy crown from drone images using segmentation. J. Korean Soc. Surv. Geod. Photogramm. Cartogr. 33(6), 605–613 (2015). https://doi.org/10.7848/ksgpc.2015.33.6.605

    Article  Google Scholar 

  15. Marsujitullah, Z. Zainuddin, S. Manjang, and A.S. Wijaya, Rice farming age detection use drone based on SVM histogram image classification, in Symposium of Nuclear Technology and Engineering Novelty (2019)

    Google Scholar 

  16. D. Stavrakoudis, D. Katsantonis, K. Kadoglidou, A. Kalaitzidis, I.Z. Gitas, Estimating rice argonomic traits using drone-collected multispectral imagery. Remote Sens. 11, 545 (2019)

    Article  Google Scholar 

  17. R. Hunt Jr., S.I. Rondon, Detection of potato beetle damage using remote sensing from small unmanned aircraft systems. J. Appl. Remote. Sens. 11(2), 026013 (2017)

    Article  Google Scholar 

  18. M. Sanfourche, B. L. Saux, A. Plyer, and G. L Besnerais, Environment Mapping & Interpretation by Drone, in 2015 Joint Urban Remote Sensing Event (JURSE) (IEEE, 2015).

    Google Scholar 

  19. J.D. Renwick, L.J. Klein, and H.F. Hamann, Drone-based reconstruction for 3D geospatial data processing, in 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT) (IEEE, 2017)

    Google Scholar 

  20. L. Wang, J. Liao, and C. Xu, Vehicle detection based on drone images with the improved Faster R-CNN, in Proceedings of the 2019 11th International Conference on Machine Learning and Computing (2019), pp. 466–471

    Google Scholar 

  21. G. Maria, E. Baccaglini, D. Brevi, M. Gavelli, and R. Scopigno, A drone-based image processing system for car detection in a smart transport infrastructure, in Proceedings of the 18th Mediterranean Electrotechnical Conference (2016)

    Google Scholar 

  22. M.H. Lee, S. Yeom, Multiple target detection and tracking on urban roads with a drone. J. Intell. Fuzzy Syst. 35, 6071–6078 (2018)

    Article  Google Scholar 

  23. T. Tang, Z. Deng, S. Zhou, L. Lei, and H. Zou, Fast vehicle detection in UAV images, in 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP), Shanghai (2017), pp. 1–5. https://doi.org/10.1109/RSIP.2017.7958795.

  24. H.D. Nguyen, I.S. Na, S.H. Kim, G.S. Lee, H.J. Yang, J.H. Choi, Multiple human tracking in drone image. Multimed. Tools Appl. 78(4), 4563–4577 (2019)

    Article  Google Scholar 

  25. S. Karim, Y. Zhang, A.A. Laghari, and M.R. Asif, Image processing based proposed drone for detecting and controlling street crimes, in 2017 IEEE 17th International Conference on Communication Technology (ICCT) (IEEE, 2018)

    Google Scholar 

  26. P. Zhu, L. Wen, X. Bian, H. Ling, Q. Hu, Vision meets drones: a challenge. arXiv 1804, 07437 (2018)

    Google Scholar 

  27. G. Leonardi, V. Barrile, R. Palamara, F. Suraci, and G. Candela, Road degradation survey through images by drone, in ed. F. Calabrò et al., vol 101 (Springer International Publishing AG, 2019), pp. 222–228. https://doi.org/10.1007/978-3-319-92102-0_24

  28. J. Seo, L. Duque, J. Wacker, Drone-enabled bridge inspection methodology and application. Automat. Constr. 94(2018), 112–126 (2018)

    Article  Google Scholar 

  29. H. Kim, S.H. Sim, and S. Cho, Unmanned aerial vehicle (UAV)-powered concrete crack detection based on digital image processing, in 6th International Conference on Advances in Experimental Structural Engineering, 11th International Workshop on Advanced Smart Materials and Smart Structures Technology, August 1–2, 2015, University of Illinois, Urbana-Champaign, United States (2015)

    Google Scholar 

  30. N.M. Shajahan, A. Sasikumar, T. Kuruvila, and D. Davis, Automated inspection of monopole tower using drones and computer vision, in 2019 2nd International Conference on Intelligent Autonomous Systems (ICoIAS) (IEEE, 2019)

    Google Scholar 

  31. A. Reddy, V.I. Gandhi, L. Ravi, V. Subramaniyaswamy, Detection of cracks and damage in wind tribune blades using artificial intelligence—based image analytics. Measurement 147, 106823 (2019). https://doi.org/10.1016/j.measurement.2019.07.051

    Article  Google Scholar 

  32. E.J. Lee, S.Y. Shin, B.C. Ko, C. Chang, Early sinkhole detection using a drone-based thermal camera and image processing. Infrared Phys. Technol. 78(2016), 223–232 (2016)

    Article  Google Scholar 

  33. K.E. Joyce, S. Duce, S.M. Leahy, J. Leon, S.W. Maier, Principles and practice of acquiring drone-based image data in marine environments. Marine and Freshwater Research 70, 952–963 (2018). https://doi.org/10.1071/MF17380

    Article  Google Scholar 

  34. A.S. Laliberte, W.J. Ripple, Automated wildlife counts from remotely sensed imagery. Wildl. Soc. Bull. 31, 362–371 (2003)

    Google Scholar 

  35. Y. Fang, S. Du, R. Abdoola, K. Djouani, C. Richards, Motion based animal detection in aerial videos. Procedia Comput. Sci. 92, 13–17 (2016)

    Article  Google Scholar 

  36. J.A. Vayssade, R. Arquet, M. Bonneau, Automatic activity tracking of goats using drone camera. Comput. Electron. Agr. 162, 767–772 (2019)

    Article  Google Scholar 

  37. Z. Fan, J. Lu, M. Gong, H. Xie, E.D. Goodman, Automatic tobacco plant detection in UAV images via deep neural networks. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 11(3), 876–887 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Information Systems Engineering, Near East University, Nicosia, Turkey

    Boran Sekeroglu & Kubra Tuncal

Authors
  1. Boran Sekeroglu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kubra Tuncal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Boran Sekeroglu .

Editor information

Editors and Affiliations

  1. Professor, Department of Artificial Intelligence, Near East University, Nicosia, Mersin 10, Turkey

    Fadi Al-Turjman

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Sekeroglu, B., Tuncal, K. (2020). Image Processing in Unmanned Aerial Vehicles. In: Al-Turjman, F. (eds) Unmanned Aerial Vehicles in Smart Cities. Unmanned System Technologies. Springer, Cham. https://doi.org/10.1007/978-3-030-38712-9_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-38712-9_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-38711-2

  • Online ISBN: 978-3-030-38712-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics