Sistema automático de registro de automóviles y manejo del historial de trabajos para la microempresa “Tecnicars David” utilizando Machine Learning

Abstract

The present research project proposes the implementation of an automatic registration system to optimize and streamline administrative processes in "Tecnicars David" Workshop, demonstrating the relevance of this work in the field of automotive service automation. The importance of the project lies in the implementation of fuzzy logic as a character recognition technique and the integration of different technologies and tools to achieve a complete and efficient system. The system utilizes fuzzy logic as a character recognition technique and machine learning for managing the history of jobs performed. The fuzzy logic technique demonstrates the system's ability to handle situations of uncertainty or blurriness in plate images, allowing for the reconstruction and completion of characters in case they are illegible or blurry. The system leverages various open-source technologies and tools. It uses the Python programming language, the OpenCV library for motion and edge detection, and fuzzy logic for data extraction from plate images. The system interface features a dashboard built on Node-Red, providing an intuitive and efficient visualization for vehicle management in the workshop. This graphical interface is tailored to the specific requirements of the workshop offering a user-friendly experience and comprehensive registration of necessary information. The automatic registration system has undergone rigorous testing demonstrating efficient and reliable operation. The system's reliability has been evaluated, achieving a high percentage of vehicle plate recognition using fuzzy logic with 90.90% effectiveness. The system has also achieved a low error rate, indicating satisfactory precision in vehicle detection and registration This has resulted in improved efficiency and enhanced vehicle management processes at "Tecnicars David" workshop.