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Unsupervised Perception Model for UAVs Landing Target Detection and Recognition

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Advanced Concepts for Intelligent Vision Systems (ACIVS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11182))

Abstract

Today, unmanned aerial vehicles (UAV) play an interesting role in the so-called Industry 4.0. One of many problems studied by companies and research groups are the sensing of the environment intelligently. In this context, we tackle the problem of autonomous landing, and more precisely, the robust detection and recognition of a unique landing target in an outdoor environment. The challenge is how to deal with images under non-controlled light conditions impacted by shadows, change of scale, perspective, vibrations, noise, blur, among others. In this paper, we introduce a robust unsupervised model allowing to detect and recognize a target, in a perceptual-inspired manner, using the Gestalt principles of non-accidentalness and grouping. Our model extracts the landing target contours as outliers using the RX anomaly detector and computing proximity and a similarity measure. Finally, we show the use of error correction Hamming code to reduce the recognition errors.

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Acknowledgments

This research is partially supported by the Mexican National Council for Science and Technology (CONACYT).

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Authors and Affiliations

  1. PSL Research University - MINES ParisTech, CMM - Center for Mathematical Morphology, Mathematics and Systems, 35, rue St. Honoré, 77305, Fontainebleau Cedex, France

    Eric Bazán & Petr Dokládal

  2. Université Paris-Est, LIGM, UMR 8049, ESIEE Paris, Cité Descartes B.P.99, 93162, Noisy le Grand Cedex, France

    Eva Dokládalová

Authors
  1. Eric Bazán
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  2. Petr Dokládal
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  3. Eva Dokládalová
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Corresponding author

Correspondence to Eric Bazán .

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Editors and Affiliations

  1. DGA, Bagneux, France

    Jacques Blanc-Talon

  2. Laboratoire XLIM, Futuroscope Chasseneuil Cedex, France

    David Helbert

  3. Ghent University, Ghent, Belgium

    Wilfried Philips

  4. CSIRO-ICT Centre, Canberra, ACT, Australia

    Dan Popescu

  5. University of Antwerp, Wilrijk, Belgium

    Paul Scheunders

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Bazán, E., Dokládal, P., Dokládalová, E. (2018). Unsupervised Perception Model for UAVs Landing Target Detection and Recognition. In: Blanc-Talon, J., Helbert, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2018. Lecture Notes in Computer Science(), vol 11182. Springer, Cham. https://doi.org/10.1007/978-3-030-01449-0_20

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  • DOI: https://doi.org/10.1007/978-3-030-01449-0_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01448-3

  • Online ISBN: 978-3-030-01449-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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