A NEURAL NETWORK MODEL FOR DETECTING CAMOUFLAGED OBJECTS

This article discusses the use of computer vision to detect camouflaged objects that are hidden by natural masks and camouflage devices. Such objects are usually varied in size, fuzzy, and visually merge with the terrain, which makes them difficult to detect. The authors analyze the models of machine learning algorithms used to segment and detect objects in images. Taking into account the analysis, to solve the problem of detecting camouflaged objects, the paper proposes a neural network model with an encoder-decoder architecture. Its features are: the use of an additional layer at the input, which is fed with an image processed by a Sobel filter, which allows to enhance the detection of object edges; the use of the convolutional stretching algorithm in the encoder blocks, in parallel with the main part of the key features determination, leads to a decrease in the dependence of detection on the size of objects; the use of a mechanism in the decoder blocks to focus on important parts of the image increases the probability of correct classification of image areas in cases of uncertainty of the model regarding their. Experiments by modeling, with different hyperparameters of the neural network, allowed us to determine that binary cross-entropy is most suitable as a loss function for solving the problem of detecting objects with strong background noise, and the choice of Parametric Rectified Linear Unit as an activation function allows to improve the quality of object segmentation. We also consider the use of various metrics to evaluate the effectiveness of the created model. Testing on datasets with real cloaked objects allowed us to identify problematic issues affecting the segmentation process in general and the accuracy of detecting cloaked objects in particular, the solution of which can improve the efficiency of neural networks in object detection. The results of the research are proposed to be used in the creation of camouflage means to determine their effectiveness, as well as to search for camouflaged enemy objects in the course of intelligence processing.