A Robust Deep Learning Approach for Automatic Iranian Vehicle License Plate Detection and Recognition for Surveillance Systems

Ali Tourani,Saeed Khazaee,Sajjad Soroori,Asadollah Shahbahrami,Ching Yee Suen

doi:10.1109/access.2020.3035992

Ali Tourani, Saeed Khazaee + Show 3 more

Open Access

https://doi.org/10.1109/access.2020.3035992

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Abstract

The process of detecting vehicles' license plates, along with recognizing the characters inside them, has always been a challenging issue due to various conditions. These conditions include different weather and illumination, inevitable data acquisition noises, and some other challenging scenarios like the demand for real-time performance in state-of-the-art Intelligent Transportation Systems (ITS) applications. This paper proposes a method for vehicle License Plates Detection (LPD) and Character Recognition (CR) as a unified application that presents significant accuracy and real-time performance. The mentioned system is designed for Iranian vehicle license plates, which have the characteristics of different resolution and layouts, scarce digits/characters, various background colors, and different font sizes. In this regard, the system uses a separate fine-tuned You Only Look Once (YOLO) version 3 platform for each of the mentioned phases and extracts Persian characters from input images in two automatic steps. For training and testing stages, a wide range of vehicle images in different challenging and straightforward conditions have been collected from practical systems installed as surveillance applications. Experimental results show an end-to-end accuracy of 95.05% on 5719 images. The test data included both color and grayscale images containing the vehicles with different distances and shooting angles with various brightness and resolution. Additionally, the system can perform the LPD and CR tasks in an average of 119.73 milliseconds for real life data, which illustrates a real-time performance for the system and usable applicability. The system is fully automated, and no pre-processing, calibration or configuration procedures are needed.

Highlights

Nowadays, Intelligent Transportation Systems (ITS) are known as the vital means of municipalities and governments for applying modern traffic planning policies
We proposed a deep model for vehicle detection based on Faster R-Convolutional Neural Networks (CNNs), which can detect the vehicles by their visual features [39]
If no license plates have been detected in the License Plate Detection (LPD) stage, the modules, including license plate cropping and character recognition are ignored, and the system waits for another input image

Summary

Introduction

Intelligent Transportation Systems (ITS) are known as the vital means of municipalities and governments for applying modern traffic planning policies. Since the outputs of these cameras are video frames or a set of images, fetching valuable results from them using machine vision and Digital Image Processing (DIP) techniques, lead to the development of numerous applications for ITS Among most of these applications, Automatic License Plate Recognition (ALPR) is considered a major step [2] [3] [4] [5] [6]. A typical ALPR system consists of three main modules to provide reasonable outcomes These three steps are known as License Plate Detection (LPD), Character Segmentation (CS), and Optical Character Recognition (OCR) which can be found in most of the approaches [2] [3] [5] [7] [14] [15]. Based on our analysis, YOLO v.3 can be a great choice for ALPR systems, especially for the CR phase, where the objects inside the license plates are rather small

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