This paper considers the application of artificial intelligence technologies, in particular computer vision, in visual information processing systems. A comprehensive analysis of neural network approaches to solving computer vision problems is carried out, including systematization of key types of problems: image classification, object detection and semantic segmentation. The architectural principles of convolutional neural networks are studied in detail with an emphasis on the mechanisms of spatial feature extraction through convolutional layers, optimization of data representation through pooling operations and feature transformation in fully connected layers. Particular attention is paid to the evolution of object detection methods, where the problem of model selection is considered as an extension of classification due to the integration of spatial coordinate regression, and an assessment of the effectiveness of detectors is carried out based on the IoU, Precision, Recall and F1-score metrics, demonstrating a fundamental trade-off between localization accuracy and processing speed. The YOLOv7 algorithm is presented as an optimal solution for real-time systems. Its architecture is based on splitting the input image into a grid of S×S cells with direct prediction of the bounding box parameters (center coordinates, width, height) and class probabilities for each cell, as well as the use of specialized layers (SPP, PANet) for multi-scale feature aggregation. The structure of the neural network confirms the effectiveness of the approach used, which ensures high performance without critically reducing accuracy in strategically important applications of video surveillance, autonomous systems, and augmented reality. A comparative study of one-stage and two-stage detectors was conducted with an assessment of their performance by key metrics. Particular attention is paid to the practical aspects of using computer vision technologies in real visual information processing systems.
