SuperDock: A Deep LearningBased Automated Floating Trash Monitoring System Floating trash in the river presents a major environmental problem that endangers the lives of river inhabitants. However, conventional trash monitoring relies on laborintensive manual inspection by dispatching inspectors to the filed, which is highly costineffective. Despite that Unmanned Aerial Vehicles (UAVs) have been widely proposed for many realtime monitoring applications, limited power supply remains the most challenging bottleneck for such UAVassisted monitoring applications. In this work, we propose an automated river trash monitoring system called SuperDock. SuperDock consists of a remote processing unit, a docking station and a UAV. SuperDock lands the UAV precisely onto the docking station followed by performing automated battery replacement. As a result, the UAV can continue the monitoring task after the battery replacement. Furthermore, SuperDock includes a deep learningassisted river trash detection module based on YOLOv3 that runs much faster than the conventional Convolution Neural Networks (CNN). In addition, a data set has been generated for training and testing the deep learning network, specifically for the floating trash detection application. Finally, SuperDock enables the UAV to communicate wirelessly with a remote computer in a realtime manner. Experimental and simulation results show that SuperDock is highly effective in monitoring the floating trash in the river.

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.