Non supervised perceptual model for target recognition in UAVs

Eric Bazan (1) , Petr Dokládal (1) , Eva Dokladalova (2)

1 CMM - Centre de Morphologie Mathématique
2 LIGM - Laboratoire d'Informatique Gaspard-Monge

Today, drones play an interesting role in the so-called Revolution 4.0. One of the problems studied by various companies and research groups are the precision landing techniques since this drone feature can be used in applications such as package delivery or object tracking. In this paper, we propose a non-supervised model that allows to detect and recognize a set of landing targets using the Gestalt principles. This proposed method is capable to recognize different coded landing targets in a robust way under outdoor non-controlled light conditions. Comparing to thresholding techniques and other methods, this work deals with image degradations caused by shadows, change of scale, noise and camera target deformation.

Domaines: Traitement des images [eess.IV]; Traitement du signal et de l'image [eess.SP]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Eric Bazan, Petr Dokládal, Eva Dokladalova. Non supervised perceptual model for target recognition in UAVs. Reconnaissance des Formes, Image, Apprentissage et Perception RFIAP, Jun 2018, Marne la Vallée, France. ⟨hal-01790867⟩