Object Detection in Images Using Deep Learning to Build Simulation Models

This article addresses the issue of traffic congestion in urban areas specifically related to intersection regulation. The second chapter focuses on an analysis of applicable computer vision technologies for traffic flow analysis. The third chapter discusses the selection of a neural network for analyzing vehicles at intersections. The fourth chapter covers data preparation and neural network training. The conclusion summarizes the obtained results. The study explores the use of computer vision techniques to improve traffic management and proposes the application of neural networks for efficient analysis of vehicle behavior at intersections. By leveraging computer vision technologies, it becomes possible to accurately monitor and regulate traffic flow, leading to improved road safety and reduced congestion. The research analyzes various neural network models and evaluates their effectiveness in vehicle detection, tracking, and classification tasks. Experimental results demonstrate promising outcomes, indicating the potential of computer vision techniques in addressing urban traffic challenges. The findings highlight the importance of data preparation and the significant role of neural network training in achieving reliable and accurate results. Overall, the study contributes to the growing body of knowledge on computer vision-based approaches for traffic analysis and offers insights into their practical implementation for addressing traffic-related issues in cities.