Vehicle Detection from Unmanned Aerial Images with Deep Mask R-CNN

In this paper, a classification approach which is applied to Mask Region-based Convolutional Neural Network as deeper is proposed for vehicle detection on the images from UAV instead of the familiar methods. The different types of unmanned aerial vehicles are widely used for a lot of areas such as agricultural spraying, advertisement shooting, fire extinguishing, transportation and surveillance, exploration, destruction for the military. In recent years, deep learning techniques are progressively developed for object detection. Segmentation algorithms based on CNN architecture are especially widely used for extracting meaningful parts of an image. Additionally, Mask R-CNN based on CNN architecture rapidly detects the object with high-accuracy on an image. This study shows that the high-accuracy results are obtained when the Mask R-CNN is applied as deeper in vehicle detection on the images taken by UAV.