Vision based indoor object detection for a drone

Abstract

Drones are a very active area of research and object detection is a crucial part in achieving full autonomy of any robot. We investigated how state-of-the-art object detection algorithms perform on image data from a drone. For the evaluation we collected a number of datasets in an indoor office environment with different cameras and camera placements. We surveyed the literature of object detection and selected to research the algorithm R-FCN (Region based Fully Convolutional Network) for the evaluation. The performances on the different datasets were then compared, showing that using footage from a drone may be advantageous in scenarios where the goal is to detect as many objects as possible. Further, it was shown that the network, even if trained on normal angled images, can be used for detecting objects in fish eye images and that usage of a fisheye camera can increase the total number of detected objects in a scene.