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Detection of landing areas for unmanned aerial vehicles

Unmanned aerial vehicles (UAV), also referred to as drones, are a growing field in computer science with applications in military systems, delivery services, emergency relief and evacuation. One of the primary obstructions to the allowance of UAV journeys over populated areas is the lack of sophisticated automated systems that detect drone landing sites. In this paper, we propose a landing area detection system using machine learning and image processing. This system compares the suitability of various features (RGB Color Model, HSV Color Model, LBP, Edge Density) in determining a suitable drop-off point. Classification on these features has been carried out using Support Vector Machines (Linear, Polynomial and RBF Kernel).