Using Deep Networks for Drone Detection

Drone detection is the problem of finding the smallest rectangle that encloses the drone(s) in a video sequence. In this study, we propose a solution using an end-to-end object detection model based on convolutional neural networks. To solve the scarce data problem for training the network, we propose an algorithm for creating an extensive artificial dataset by combining background-subtracted real images. With this approach, we can achieve precision and recall values both of which are high at the same time.Comment: To appear in International Workshop on Small-Drone Surveillance, Detection and Counteraction Techniques organised within AVSS 201

Dynamics of stable viscous displacement in porous media

We investigate the stabilization mechanisms of the invasion front in two-dimensional drainage displacement in porous media by using a network simulator. We focus on the process when the front stabilizes due to the viscous forces in the liquids. We find that the capillary pressure difference between two different points along the front varies almost linearly as function of height separation in the direction of the displacement. The numerical results support arguments that differ from those suggested earlier for viscous stabilization. Our arguments are based upon the observation that nonwetting fluid flows in loopless strands (paths) and we conclude that earlier suggested theories are not suitable to drainage when nonwetting strands dominate the displacement process. We also show that the arguments might influence the scaling behavior between the front width and the injection rate and compare some of our results to experimental work.Comment: The paper has been substantially revised. 12 papes, 10 figure