Title
Synthetic Image Generation For Training Deep Learning-Based Automated License Plate Recognition Systems On The Brazilian Mercosur Standard
Abstract
License plates are the primary source of vehicle identification data used in a wide range of applications including law enforcement, electronic tolling, and access control amongst others. License plate detection (LPD) is a critical process in automatic license plate recognition (ALPR) that reduces complexity by delimiting the search space for subsequent ALPR stages. It is complicated by unfavourable factors including environmental conditions, occlusion, and license plate variation. As such, it requires training models on substantial volumes of relevant images per use case. In 2018, the new Mercosur standard came in to effect in four South American countries. Access to large volumes of actual Mercosur license plates with sufficient presentation variety is a significant challenge for training supervised models for LPD, thereby adversely impacting the efficacy of ALPR in Mercosur countries. This paper presents a novel license plate embedding methodology for generating large volumes of accurate Mercosur license plate images sufficient for training supervised LPD. We validate this methodology with a deep learning-based ALPR using a convolutional neural network trained exclusively with synthetic data and tested with real parking lot and traffic camera images. Experiment results achieve detection accuracy of 95% and an average running time of 40 ms.
Year
DOI
Venue
2021
10.1007/s10617-020-09241-7
DESIGN AUTOMATION FOR EMBEDDED SYSTEMS
Keywords
DocType
Volume
Mercosur license plates, Automated license plate recognition, License plate detection, Number plate detection, Smart cities, Deep learning, Synthetic data
Journal
25
Issue
ISSN
Citations 
2
0929-5585
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Gilles Silvano100.68
Vinícius Ribeiro200.68
Vitor Greati300.34
Aguinaldo Bezerra400.68
Ivanovitch M. D. Silva58713.56
Patricia Endo612125.32
Theo Lynn711622.40