NDLI: License plate segmentation and recognition system using deep learning and OpenVINO

Content Provider	IET Digital Library
Author	Zunti, Riel D. Castro Yépez, Juan Ko, Seok Bum
Abstract	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.
Starting Page	119
Ending Page	126
Page Count	8
ISSN	1751956X
Volume Number	14
e-ISSN	17519578
Issue Number	Issue 2, Feb (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/14/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2019.0481
Journal	IET Intelligent Transport Systems
Publisher Date	2020-01-09
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automatic License Plate Recognition Solutions Caltech Cars Dataset Computer Vision And Image Processing Technique Convolutional Neural Nets Deep Learning Depthwise Separable Convolutions Embedded System Feature Extraction Feature Extractor Image Recognition Image Segmentation Intelligent Transport System Knowledge Engineering Technique Learning in AI License Plate Segmentation And Recognition System Neural Computing Technique Object Detection OpenVINO Recognition Accuracy Recognition System Single Shot Detector Traffic Engineering Computing UCSD-Stills Dataset
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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