A Convolutional Neural Network Feature Detection Approach to Autonomous Quadrotor Indoor Navigation Object detection, extended to recognize and localize indoor structural features, is used to enable a quadrotor drone to autonomously navigate through indoor environments. The video stream from a monocular frontfacing camera onboard a quadrotor drone is fed to an offboard system that runs a Convolutional Neural Network (CNN) object detection algorithm to identify specific features such as deadends, doors, and intersections in hallways. Using pixelscale dimensions of the bounding boxes around the recognized objects, the distance to intersections, deadends and doorways can be estimated accurately using a Support Vector Regression (SVR) model to generate flight control commands for consistent realtime autonomous navigation at flight speeds approaching 2 ms.

Counting Vehicles with Deep Learning in Onboard UAV Imagery The integration of mobile and ubiquitous computing with deep learning methods is a promising emerging trend that aims at moving the processing task closer to the data source rather than bringing the data to a central node. The advantages of this approach range from bandwidth reduction, high scalability, to high reliability, just to name a few. In this paper, we propose a realtime deep learning approach to automatically detect and count vehicles in videos taken from a UAV (Unmanned Aerial Vehicle). Our solution relies on a convolutional neural networkbased model finetuned to the specific domain of applications that is able to precisely localize instances of the vehicles using a regression approach, straight from image pixels to bounding box coordinates, reasoning globally about the image when making predictions and implicitly encoding contextual information. A comprehensive experimental evaluation on realworld datasets shows that our approach results in stateoftheart performances. Furthermore, our solution achieves realtime performances by running at a speed of 4 Frames Per Second on an NVIDIA Jetson TX2 board, showing the potentiality of this approach for realtime processing in UAVs.