Sistema para detección de automóviles robados empleando visión artificial en vehículo aéreo no tripulado (UAV)

Abstract

The present research focuses on the design and implementation of an advanced technological system for the detection of stolen cars, integrating various technologies such as computer vision, YOLOv8 convolutional neural networks, optical character recognition (OCR) and unmanned aerial vehicles (UAV). This innovative system captures, processes and analyzes images of vehicles in parking lots, allowing license plates to be identified and compared with cloud databases managed in Azure. The results obtained are notified through a web platform and applications such as Telegram and email, thus facilitating an agile and efficient response. The system is composed of a UAV equipped with an FPV camera that transmits real-time video to a receiver connected to a computer. The processed information is stored in a database and automatically verified. The UAV configuration allows stable and safe flights, while the artificial intelligence models used guarantee high accuracy in license plate detection, even under variable lighting conditions. In addition, field tests were carried out in different locations to evaluate the system's performance in terms of flight time, energy consumption and data processing. Among the most outstanding results is an 82.78% reliability rate in the detection of vehicle license plates with average information sending times of 20.26 seconds per detected event and sending of respective notifications. This project represents a significant contribution to strengthening public security by providing scalable and technologically advanced.