Drone Detection Using Convolutional Neural Networks
This addresses drone detection for surveillance or security applications, but it is incremental as it applies existing methods to a specific dataset.
The paper tackled drone detection from fisheye camera images by comparing convolutional neural networks (CNN), support vector machines (SVM), and nearest neighbor methods, achieving accuracies of 95%, 88%, and 80%, respectively.
In image processing, it is essential to detect and track air targets, especially UAVs. In this paper, we detect the flying drone using a fisheye camera. In the field of diagnosis and classification of objects, there are always many problems that prevent the development of rapid and significant progress in this area. During the previous decades, a couple of advanced classification methods such as convolutional neural networks and support vector machines have been developed. In this study, the drone was detected using three methods of classification of convolutional neural network (CNN), support vector machine (SVM), and nearest neighbor. The outcomes show that CNN, SVM, and nearest neighbor have total accuracy of 95%, 88%, and 80%, respectively. Compared with other classifiers with the same experimental conditions, the accuracy of the convolutional neural network classifier is satisfactory.