This paper presents an intelligent software solution for object identification in images using deep learning models, designed for automated interpretation of monitoring results of aviation objects and infrastructure. The proposed approach addresses the growing demand for enhanced flight safety and improved efficiency of aviation operations. To meet this demand, a three-level model is proposed: Level 1 performs object detection, Level 2 provides optical character recognition (OCR) and text normalization, and Level 3 implements fuzzy matching with an object database. Based on comparative testing of detection models, YOLOv8n was selected as the core of the three-level architecture, providing an optimal balance between real-time processing speed and detection accuracy. A detailed analysis of model architectures revealed specific advantages and limitations in identifying monitoring results from image data. Training on a specialized dataset and subsequent testing confirmed the high efficiency of the proposed solution and its ability to reliably localize objects even under challenging visual conditions such as shadows, glare, and partial occlusion. The
obtained results demonstrate the significant potential of the proposed intelligent solution for extending computer vision capabilities in the monitoring of aviation objects and infrastructure. The experimental results also confirm the effectiveness of the OCR and fuzzy matching modules in improving object identification accuracy under real-world conditions.
