Automatic Vehicle License Plate Recognition Using Optimal K-Means With Convolutional Neural Network for Intelligent Transportation Systems

Irina Valeryevna Pustokhina,Joel J P C Rodrigues,Ashish Khanna,Deepak Gupta,K Shankar,Denis Alexandrovich Pustokhin,Changho Seo,Gyanendra Prasad Joshi

doi:10.1109/access.2020.2993008

Abstract

Due to recent developments in highway research and increased utilization of vehicles, there has been significant interest paid on latest, effective, and precise Intelligent Transportation System (ITS). The process of identifying particular objects in an image plays a crucial part in the fields of computer vision or digital image processing. Vehicle License Plate Recognition (VLPR) process is a challenging process because of variations in viewpoint, shape, color, multiple formats and non-uniform illumination conditions at the time of image acquisition. This paper presents an effective deep learning-based VLPR model using optimal K-means (OKM) clustering-based segmentation and Convolutional Neural Network (CNN) based recognition called OKM-CNN model. The proposed OKM-CNN model operates on three main stages namely License Plate (LP) detection, segmentation using OKM clustering technique and license plate number recognition using CNN model. During first stage, LP localization and detection process take place using Improved Bernsen Algorithm (IBA) and Connected Component Analysis (CCA) models. Then, OKM clustering with Krill Herd (KH) algorithm get executed to segment the LP image. Finally, the characters in LP get recognized with the help of CNN model. An extensive experimental investigation was conducted using three datasets namely Stanford Cars, FZU Cars and HumAIn 2019 Challenge dataset. The attained simulation outcome ensured effective performance of the OKM-CNN model over other compared methods in a considerable way.

Highlights

The recent developments in intelligent transportation systems (ITS) and Graphical Processing Units (GPU) led to major attention being bestowed upon Automatic Vehicle License Plate Recognition (VLPR) in several research domains
license plate recognition (LPR) is considered to be highly significant in various applications like unmanned parking fields, security management of unattended regions as well as traffic safety
This paper presents an effective DL-based VLPR model using optimal K-means (OKM) clustering-based segmentation and Convolutional Neural Network (CNN)-based recognition called OKM-CNN model

Summary

INTRODUCTION

The recent developments in intelligent transportation systems (ITS) and Graphical Processing Units (GPU) led to major attention being bestowed upon Automatic Vehicle License Plate Recognition (VLPR) in several research domains. Character segmentation was attained in the previous study under the application morphology, relaxation labeling, as well as linked components [3] It has been composed with a maximum count of character analyzing methodologies as reported in the literature [4] such as Baye’s classification, Artificial Neural Networks (ANN), Fuzzy C-Means (FCM), Support Vector Machine (SVM), Markov chain model, and K-Nearest Neighbor (kNN) classifier. Even though these methods are able to compute the task of placing an LP segmentation and analysis, several models perform only on individual line character segmentation and two kinds of character analyses were established namely, English and numerals.

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CONCLUSION