Expanding small UAV capabilities with ANN : a case study for urban areas observation

Rodrigo L. Mota (1) , Luiz F. Felizardo (1) , Elcio H. Shiguemori (2) , Alexandre Carlos Brandao-Ramos (1) , Felix Mora-Camino (3)

1 Institute of Mathematics and Computing
2 Institute of Advanced Studies
3 MAIAA - ENAC - Laboratoire de Mathématiques Appliquées, Informatique et Automatique pour l'Aérien

Autonomous Unmanned Aerial Vehicles (UAVs) are available alternatives for urban areas inspections due to its cost and safety when compared to other traditional methods. The purpose of this paper is to report the development of a system capable of analyzing digital images of the terrain and identifies potential invasion, unauthorized alterations on the ground and deforestation in some areas of special use. Images are captured by a camera coupled to an autonomous helicopter, which flight around the area. For the processing of the images an artificial neural network technique called Kohonen SOM (Self Organized Map) will be used. The processing is actually a set of steps that seek to collate the final common characteristics of a given image.

Mots clés: pattern recognition; inspection; UAV; autonomous helicopter; Kohonen SOM

Domaines: Sciences de l'ingénieur [physics]; Automatique / Robotique

Rodrigo L. Mota, Luiz F. Felizardo, Elcio H. Shiguemori, Alexandre Carlos Brandao-Ramos, Felix Mora-Camino. Expanding small UAV capabilities with ANN : a case study for urban areas observation. ICIIP 2013, IEEE Second International Conference on Image Information Processing, Dec 2013, Shimla, India. pp.516 - 520, ⟨10.1109/ICIIP.2013.6707646⟩. ⟨hal-01078496⟩