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

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.

[1]  D. Neef.  A Little Knowledge Is a Dangerous Thing , 1998 .

[2]  Gaurav S. Sukhatme,et al.  Visual servoing of an autonomous helicopter in urban areas using feature tracking , 2006, J. Field Robotics.

[3]  Bernard Barrett,et al.  A little knowledge is a dangerous thing ... , 2005, Health information and libraries journal.

[4]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[5]  Stefan Hrabar,et al.  Development of an autonomous helicopter for aerial powerline inspections , 2010, 2010 1st International Conference on Applied Robotics for the Power Industry.

[6]  Yi Wang,et al.  Sensor fusion method using GPS/IMU data for fast UAV surveillance video frame registration , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[7]  Yvonne Gibbs.  Ground Control Station , 2015 .

[8]  José Alberto Quintanilha.  Processamento de imagens digitais , 1990 .

[9]  M. Friedman,et al.  A Little Knowledge Is a Dangerous Thing , 2007 .

[10]  Michal Pechoucek,et al.  Autonomous UAV Surveillance in Complex Urban Environments , 2009, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.