APPLICATION OF A CONVOLUTIONAL NEURAL NETWORK AND A KOHONEN NETWORK FOR ACCELERATED DETECTION AND RECOGNITION OF OBJECTS IN IMAGES

• Main Authors: Victor Skuratov, Konstantin Kuzmin, Igor Nelin, Mikhail Sedankin
• Format: Article
• Language: English
• Published: Scientific Route OÜ 2020-07-01
• Series: EUREKA: Physics and Engineering
• Subjects: pattern recognition; object search; kohonen neural network; convolutional neural network; radar and satellite images
• Online Access: http://journal.eu-jr.eu/engineering/article/view/1360

One of the most effective ways to improve the accuracy and speed of algorithms for searching and recognizing objects in images is to pre-select areas of interest in which it is likely to detect objects of interest. To determine areas of interest in a pre-processed radar or satellite image of the underlying surface, the Kohonen network was used. The found areas of interest are sent to the convolutional neural network, which provides the final detection and recognition of objects. The combination of the above methods allows to speed up the process of searching and recognizing objects in images, which is becoming more expedient due to the constantly growing volume of data for analysis. The process of preliminary processing of input data is described, the process of searching and recognizing patterns of aircraft against the underlying surface is presented, and the analysis of the results is carried out. The use of the Kohonen neural network makes it possible to reduce the amount of data analyzed by the convolutional network by 18–125 times, which accordingly accelerates the process of detection and recognition of the object of interest. The size of the parts of the input image fed into the convolutional network, into which the zones of interest are divided, is tied to the image scale and is equal to the size of the largest detectable object, which can significantly reduce the training sample. Application of the presented methods and centering of the object on training images allows accelerating the convolutional network training by more than 5 times and increasing the recognition accuracy by at least 10%, as well as minimizing the required minimum number of layers and neurons of the network by at least halving, respectively increasing its speed