Abstract
This article is dedicated to the development and investigation of methods for automatic object recognition using artificial intelligence and convolutional neural networks. A standard dataset for object recognition, including images of various types, was used as the experimental base, allowing the creation of a model capable of effectively classifying objects under various conditions, such as changes in lighting, viewing angles, and image quality. The methodology includes data normalization and augmentation, stratified dataset splitting, and model architecture optimization to ensure a balance between computational efficiency and classification accuracy. The developed convolutional neural network features a layered architecture with activation functions and regularization methods, enabling the model to remain robust and adaptive. The results of the study demonstrated the high accuracy and reliability of the proposed model. The analysis of classification errors identified challenges related to the recognition of visually similar objects and proposed solutions to minimize them. This work emphasized the significance of using artificial intelligence and convolutional neural networks for automating object recognition, opening new perspectives for their application in intelligent systems and high-precision technologies.