APPLICATION OF NEURAL NETWORKS IN PATTERN RECOGNITION

Abstract

This article is devoted to the application of neural networks in the problem of pattern recognition. The paper considers the relevance and significance of the problem of image classification, and describes the main methods and approaches used in neural networks to solve this problem.

The first chapter describes the main advantages of using neural networks in the problem of pattern recognition. The issues of improving classification accuracy, adapting models to various tasks, improving interpretability and processing of temporary data are considered.

The second chapter is devoted to the application of neural networks in the task of detecting objects. Various architectures such as R-CNN, SSD and YOLO are considered, and methods of training and optimizing models for object detection are described.

The third chapter discusses the challenges and prospects in the field of object detection using neural networks. The problems of accuracy and speed, working with various objects and conditions, real-time detection, as well as adaptation to new classes of objects are described.

In conclusion, the results of the work are summarized and directions for future research in the field of the use of neural networks in pattern recognition are indicated.