A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

Automatic License Plate Recognition (ALPR) has been a frequent topic of research due to many practical applications. However, many of the current solutions are still not robust in real-world situations, commonly depending on many constraints. This paper presents a robust and efficient ALPR system based on the state-of-the-art YOLO object detector. The Convolutional Neural Networks (CNNs) are trained and fine-tuned for each ALPR stage so that they are robust under different conditions (e.g., variations in camera, lighting, and background). Specially for character segmentation and recognition, we design a two-stage approach employing simple data augmentation tricks such as inverted License Plates (LPs) and flipped characters. The resulting ALPR approach achieved impressive results in two datasets. First, in the SSIG dataset, composed of 2,000 frames from 101 vehicle videos, our system achieved a recognition rate of 93.53% and 47 Frames Per Second (FPS), performing better than both Sighthound and OpenALPR commercial systems (89.80% and 93.03%, respectively) and considerably outperforming previous results (81.80%). Second, targeting a more realistic scenario, we introduce a larger public dataset, called UFPR-ALPR dataset, designed to ALPR. This dataset contains 150 videos and 4,500 frames captured when both camera and vehicles are moving and also contains different types of vehicles (cars, motorcycles, buses and trucks). In our proposed dataset, the trial versions of commercial systems achieved recognition rates below 70%. On the other hand, our system performed better, with recognition rate of 78.33% and 35 FPS.Comment: Accepted for presentation at the International Joint Conference on Neural Networks (IJCNN) 201

Reconhecimento de placas veiculares utilizando deep learning : análise da influência de dados sintéticos no processo de reconhecimento

Trabalho de conclusão de curso (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Curso de Graduação em Engenharia de Controle e Automação, 2018.O reconhecimento automático de placas de veículos tem sido alvo de diversos estudos, dada a sua aplicabilidade em situações reais: cobrança de pedágio, identificação de veículos em estacionamentos ou mesmo por questões de segurança em controle de veículos que cruzam fronteiras entre os países. O trabalho desenvolvido aqui consiste em retreinar tanto um modelo de reconhecimento de placas como um modelo de reconhecimento de objetos diversos, utilizando bases de dados sintéticas de placas no padrão brasileiro com variações de rotação, tamanho e ruído. Assim, a influência da utilização de placas sintéticas na acurácia de sistemas responsáveis por localizar placas reais, segmentar os caracteres e reconhecê-los foi avaliada e nos testes realizados houve um aumento da acurácia (em relação a um sistema treinado com placas reais) de três etapas: segmentação dos caracteres, reconhecimento de letras e reconhecimento dos números (2,54%, 1,09% e 2,49% respectivamente). Destaca-se a acurácia de 62,47% para a etapa de reconhecer os números, obtida por uma rede neural treinada exclusivamente com dados sintéticos e testada em placas reais.The Automatic License Plate Recognition has been the subject of several studies, given its applicability in real situations: toll collection, identification of vehicles in parking lots or even for safety issues in vehicle control that cross borders between countries. The work developed here consists of retraining both a plate recognition model and a deep neural network for object detection, using synthetic plates databases in the Brazilian standard with variations of rotation, size and noise. Thus, the influence of the use of synthetic plates on the accuracy of systems responsible for locating real plates, segmenting the characters and recognizing them was evaluated and in the tests performed there was an increase in accuracy (considering a system trained with real plates) of three stages: character segmentation, letter recognition and number recognition (2.54 %, 1.09 % and 2, 49 % respectively). It stands out the accuracy of 62.47 % (in the number recognition step) obtained by a neural network trained exclusively with synthetic data and tested on real plates