Implementación de un sistema inteligente para la identificación vehicular

Abstract

The main objective of this research work was to implement an intelligent system capable of classifying vehicle and automotive license plates, as well as self-correcting recognition errors, for which it was based on the design of an algorithm capable of detecting and classifying vehicles, implementing artificial intelligence. Once the process to be followed was identified, a source code was implemented for the detection of the types of license plates using the convolutional neural network WPOD in which the data of the edge, width and height of the plate were specified so that it only provides the photo of license plate. For the binarization process used in the research, the Otsu algorithm was used, which converts the images into the gray, blur, binary and dilation scales, applying filters that can obtain the location segments of the letters and numbers. Finally, an effective system was obtained, with acceptable detection capacity, since design parameters of the architecture of each type of red were established, which achieved a satisfactory solution to the problem of identification, classification and validation of the characters of Ecuadorian license plates.