Using Deep Networks for Drone Detection
This work addresses drone detection for surveillance or security applications, but it is incremental as it applies existing object detection methods to a specific domain with data augmentation.
The paper tackles drone detection in video by proposing an end-to-end convolutional neural network model, addressing data scarcity with an algorithm to generate an artificial dataset from background-subtracted real images, achieving high precision and recall values.
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.