A generalized higher order neural network for aircraft recognition in a video docking system
In modern world of today where air traffic is continuously increasing and available space at the airports remains finite, there is a problem of safe docking of aircraft. The problem needs to be solved to ensure safe and smooth movement of aircraft, passengers and crew while making optimum utilization of available ground space. Without such systems having in place, accidents keep occurring due to human judgment errors. These accidents are causing loss of material costs and human injury. The importance of Video-based Docking Systems is continuously increasing due to the challenges of current and upcoming traffic demands of future. This paper evaluates two neural networks architectures for recognition of civil airliners in a Video docking system. The networks compared are feedforward neural network and probabilistic neural network. The results are compared by presenting data to neural networks while deforming the shape, adding noise and partly occluding the shape and presenting those angles for which network was not trained.
neural networks aircraft recognition video docking system feedforward neural network probabilistic neural network