Analýza obsazenosti parkovacích míst pomocí neuronových sítí

Abstract

The goal of this thesis is to focus on parking occupancy analysis using advanced machine learning and computer vision techniques. The aim is to develop a system that can accurately identify and classify occupied and free parking spaces based on image data. The work uses publicly available datasets of parking lot images and applies various neural network models to them to determine which methods are the most efficient and accurate. Neural networks are researched for their potential to offer high accuracy in parking occupancy detection.