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Prediction of Object Position from Aerial Images Utilising Neural Networks

This paper presents the method for object detection from aerial images and position estimation of individual objects using a single camera and a fixed point drone with GNSS. There are two possible approaches of estimated GNSS position for objects detected in image captured by Unmanned Aerial Vehicle (UAV). First method is based on UAV camera specifications and is using mathematical approach (trigonometry). Second method is using Haar’s cascade filters for object localisation in image. For object detection in an image there has been selected Retina Net which has showed most promising results for our application compared to yolo v3 and yolo v4 tiny. Despite the limitation of GNSS precision, the presented method proved to be efficient.