METHOD FOR SEMANTIC IMAGE SEGMENTATION BASED ON THE NEURAL NETWORK WITH GABOR FILTERS

The article is devoted to the use of Gabor filters to improve the efficiency of convolutional neural networks (CNN) in image analysis tasks, in particular, segmentation. The application of Gabor filters as an adaptive component in the initial layers of CNN is considered, which allows improving the extraction of texture and structural features. To achieve an optimal balance between the number of trainable parameters and accuracy, adaptive Gabor filters are proposed, which increase the number of input channels without significantly complicating the model. A comparative analysis of architectures using PSPNet for image segmentation modified with adaptive Gabor filters is carried out. Limitations on the size of filters that ensure acceptable computational costs are considered. The relevance of the approach on a dataset for image segmentation is confirmed, demonstrating an improvement in accuracy with a minimal increase in the number of parameters.