Detection of Airplanes on the Ground Using YOLO Neural Network

Abstract

The presented paper benchmarks the performance of state-of-the-art methods of objects detection in the particular case of airplanes on the ground identification detection in aerial images taken from unmanned aerial vehicles or satellites. There were tested two popular single-stage neural networks YOLO v.3 and Tiny YOLO v.3 based on the “You Only Look Once” approach. The considered artificial neural network architectures for objects detection has been trained and applied over the specifically created image database. Experimental verification proves their high detection ability, location precision and realtime processing speed using modern graphics processing unit. That approach can be easily applied for detection of many different classes of ground objects.