Intelligent license plate recognition system in real time
Students Name: Bednarchuk Markiian Olehovych
Qualification Level: magister
Speciality: Information Systems and Technologies
Institute: Institute of Computer Science and Information Technologies
Mode of Study: full
Academic Year: 2023-2024 н.р.
Language of Defence: ukrainian
Abstract: The work is devoted to the development and implementation of an intelligent license plate recognition system in real time. Recognition of license plates is an urgent task in modern conditions, in particular in the areas of traffic control, security and automated data processing. The work uses advanced methods of computer vision and deep learning to create an efficient and fast recognition system based on a video stream. The existing methods of object recognition are analyzed in detail and the optimal approaches for implementing the system in real time are selected. Advanced deep learning algorithms such as Convolutional Neural Networks (CNN) are used to improve recognition accuracy and ensure adaptability to different operating conditions[1]. At this stage of the research, a thorough analysis of existing object recognition methods, in particular vehicle license plates, was conducted. The analytical review covered a wide range of technical approaches, including classical computer vision techniques and modern deep learning methods. The most important, most popular cryptocurrency with the largest capitalization, reaching half a trillion dollars, is bitcoin. The main advantage of this cryptocurrency is that the transactions carried out in the system are secure, fast and anonymous due to cryptographic methods and blockchain algorithm. Due to the convincing positive aspects of this asset, bitcoin has contributed to the emergence of other cryptocurrencies that are directly dependent on it [2]. Convolutional neural networks are known for their ability to automatically learn representations of input data, including spatial features. This is particularly useful in object recognition tasks, where important information is often encoded in the spatial relationships between image pixels. The use of such algorithms allows not only to improve the accuracy of license plate recognition, but also to create a system that can adapt to different operating conditions. This includes the ability to work in different lighting, viewing angles and other factors that may arise in real traffic conditions. This approach allows the system to maintain high efficiency in real time even under variable operating conditions. The main part of the work is devoted to the development of the system architecture and software implementation. The practicalities of implementing the system in a variety of scenarios are considered, including large amounts of data, different types of license plates, and lighting conditions. Great attention is paid to optimizing performance and ensuring low latency to achieve real response time. Study object - is the process of recognizing license plates in real time. Scope of research - is the methods and means of detecting and recognizing license plates. Goal of research: the purpose of this master’s thesis is the development and implementation of an intelligent system capable of automatically recognizing license plates on road signs in real time. The main goal is to improve road safety, simplify traffic control and optimize transport processes. The result of the design and development is the creation of an effective real-time intelligent license plate recognition system, which can be used for automatic traffic control, increasing traffic safety, simplifying traffic control and improving the overall efficiency of transport processes. The work results demonstrate high recognition accuracy and video stream processing speed. The system has been successfully implemented in practical tasks such as automated traffic control, where it provides an effective tool for detecting vehicles and controlling their movements in real time. The obtained work makes a significant contribution to the development of the field of object recognition and is an actual step in the direction of improving transport control and security systems. High indicators of efficiency and reliability make it promising for implementation in various areas where automation and accurate recognition of license plates in real time are important. Keywords: information system, object detection, roads, analysis. ¬ List of used literary sources. References. 1. Introduction to Convolution Neural NetworkA [Electronic resource]. – Access mode: https://www.geeksforgeeks.org/introduction-convolution-neural-network/.