Reconhecimento de placas de automóveis e de seus condutores para a abertura automática de portões.

Abstract

This work aims to contribute with the assistance in the entry and exit of cars in buildings and private condominiums. Aiming to achieve this goal, this work was divided into two stages: development of a neural network algorithm capable of identify and recognize the license plate of a car and development of an algorithm capable of identify and recognize the driver. For the license plates identification, the pre-trained Tensorflow network model was used, called MobileNet-SSD, intended for object detection. For the recognition of the car’s license plate, OCR optical character recognition technology was used. The algorithm implementation was performed in Python. The deep convolutional neural network showed good results of precision, recall and F1-score, neural network performance metrics, which could be improved in future works by performing a pre-processing in the chosen database. For the identification and facial recognition of the conductor, an oriented gradient histogram feeding a support vector machine is used. The dlib and face_recognition libraries were used. Satisfactory results of accuracy, precision, recall and F1-score were obtained, above 98.7%, which demonstrates the predictive fidelity of the implemented facial recognition algorithm.