Avaliação de plataformas para o reconhecimento de placas veiculares brasileiras

Abstract

With the growing number of private vehicles in Brazil, better methods for managing and inspecting the vehicle fleet is becoming increasingly necessary. License plates (LP) are unique and mandatory objects with the purpose of identifying the vehicle as well as its owner. It is recommended that the efficient collection of information on license plates be performed by automated systems for LP detection and recognition. These systems are fundamental for the supervision and management of different activities related to vehicle traffic. In this regard, this paper presents a study that identifies methods for LP detection and recognition with algorithms based on machine learning and deep learning. To produce this experiment, we succeeded in collecting an image bank of vehicles in toll plazas that are located in the municipality of Cabo de Santo Agostinho - PE and provide access to the Governador Eraldo Gueiros Port Industrial Complex - SUAPE. The objective of this work was to provide a comparison between Microsoft Azure's computer vision service for LP object detection in conjunction with Google Vision's Optical Character Recognition (OCR) services with the YOLO v4 Deep Learning algorithm. The result of the experiment showed that under similar configuration conditions in both models studied, YOLO v4 performed better, achieving a 92% precision rate in license plate detection and recognition.