NDLI: Deep learning-based embedded license plate localisation system

Content Provider	IET Digital Library
Author	Yépez, Juan Zunti, Riel D. Castro Ko, Seok Bum
Abstract	In this study the authors propose novel neural network architecture for license plate localisation (LPL) based on an inverted residual structure where the shortcut connections are between the linear bottleneck layers. This residual structure is used for feature extraction in a modified single shot detector for object detection, where standard convolutions are replaced with depthwise separable convolutions in classification layers. The proposed deep learning (DL) solution was tested against three popular international research databases and achieves state-of-the-art results, proving that the proposed model is accurate and robust. Across those databases, the proposed model surpasses other recent LPL works, including DL-based methods, in terms of accuracy and speed. The authors show the proposed architecture to have significant speedup and computational efficiency gains over other DL models, and to have fast per-image localisation processing times sufficient for applications deployed on expensive and commodity hardware alike. Using a novel multi-threading video capture with motion detection then inference algorithm, the authors increase computational efficiency and drop fewer frames overall, allowing for increased performance. Repeated tests show that the proposed method is well-suited to real-time and highly accurate LPL, regardless of hardware.
Starting Page	1569
Ending Page	1578
Page Count	10
ISSN	1751956X
Volume Number	13
e-ISSN	17519578
Issue Number	Issue 10, Oct (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/13/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2019.0082
Journal	IET Intelligent Transport Systems
Publisher Date	2019-07-02
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Classification Layer Computer Vision And Image Processing Technique Deep Learning Depthwise Separable Convolutions Embedded License Plate Localisation System Fast Per-image Localisation Feature Extraction Image Capture Image Classification Image Motion Analysis Image Recognition Inference Algorithm Inference Mechanisms Inverted Residual Structure Learning in AI Linear Bottleneck Layer Modified Single Shot Detector Motion Detection Multi-threading Multithreading Video Capture Neural Computing Technique Neural Net Architecture Neural Network Architecture Object Detection Parallel Software Traffic Engineering Computing Video Signal Processing
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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