Study of the efficiency of detecting and recognizing drone images from a video stream - Зубков, О.В.; Шейко, С.А.; Олейников, В.Н.; Карташов, В.М.; Корытцев, И.В.; Бабкин, С.И. (Radiotekhnika, 2020)
The authors have developed and experimentally tested an algorithm for processing a video stream of a stationary video camera. It consists of the stages of detecting moving objects and classifying these objects using a neural network. To detect moving objects, the methods of identifying moving objects against a stationary background and analyzing the history of motion were used. Based on the experimental data, the effectiveness of using the models of the background images of MOG, MOG2, KNN, GMG, CNT, GSOC, LSBP for solving the problem was analyzed. Recommendations for the choice of the parameters of these models were formulated. The selection criteria were as follows: high performance and low noise. Models of fully connected and convolutional neural networks were created and trained making it possible to classify 12 types of moving objects. Sets of images were created to train neural networks: drones, fragments of tree foliage, grass, clouds and insects. Based on the results of training and testing networks, recommendations are given for the number of network layers, the number of neurons in a layer, the number of convolutions to achieve maximum performance and recognition accuracy. Comparative analysis of the accuracy of drone classification using fully connected and convolutional networks when processing experimental data has proven the effectiveness of using convolutional networks. The dependence of the drone detection accuracy on the image size and, accordingly, on the distance to this drone is plotted.