Open AccessLicense plate segmentation and recognition system using deep learning and OpenVINO
Author(s) -
CastroZunti Riel D.,
Yépez Juan,
Ko SeokBum
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2019.0481
Subject(s) - computer science , segmentation , artificial intelligence , feature (linguistics) , license , feature extraction , pattern recognition (psychology) , artificial neural network , xeon , extractor , deep learning , computer vision , parallel computing , engineering , operating system , philosophy , linguistics , process engineering
As Intelligent Transportation Systems applications increase in prevalence, Automatic License Plate Recognition solutions must be made continually faster and more accurate. The authors propose an embedded system for fast and accurate license plate segmentation and recognition using a modified single shot detector (SSD) with a feature extractor based on depthwise separable convolutions and linear bottlenecks. The feature extractor requires less parameters than the original SSD + VGG implementation, enabling fast inference. Tested on the Caltech Cars dataset, the proposed model achieves 96.46% segmentation and 96.23% recognition accuracy. Tested on the UCSD‐Stills dataset, the proposed model achieves 99.79% segmentation and 99.79% recognition accuracy. The authors achieve a per‐plate (resized to 300 × 300 px) processing time of 59 ms on an Intel Xeon CPU with 12 cores (2.60 GHz per core), 14 ms using the same CPU and OpenVINO (a neural network acceleration platform), and 66 ms using the proposed low‐cost Raspberry Pi 3 and Intel Neural Compute Stick 2 with OpenVINO embedded system.