Edge-AI-Based Real-Time Automated License Plate Recognition System

Cheng-Jian Lin,Hsueh-Yi Lin,Chen-Chia Chuang

doi:10.3390/app12031445

Abstract

The rapid development of urban intelligence has turned intelligent transport system (ITS) development into a primary goal of traffic management. Automated license plate recognition (ALPR) for moving vehicles is a core aspect of ITS. Most ALPR systems send images back to a server for license plate recognition. To reduce delays and bandwidth use during image transmission, this study proposes an edge-AI-based real-time ALPR (ER-ALPR) system, in which an AGX XAVIER embedded system is embedded on the edge of a camera to achieve real-time image input to an AGX edge device and to enable real-time automatic license plate character recognition. To assess license plate characters and styles in a realistic setting, the proposed ER-ALPR system applies the following approaches: (1) image preprocessing; (2) You Only Look Once v4-Tiny (YOLOv4-Tiny) for license plate frame detection; (3) virtual judgment line for determining whether a license plate frame has passed; (4) the proposed modified YOLOv4 (M-YOLOv4) for license plate character recognition; and (5) a logic auxiliary judgment system for improving license plate recognition accuracy. We tested the proposed ER-ALPR system in selected real-life test environments in Taiwan. In experiments, the proposed ER-ALPR system achieved license plate character recognition rates of 97% and 95% in the day and at night, respectively. Through the AGX system, the proposed ER-ALPR system achieves a high recognition rate at a low computational cost.

Highlights

Governments worldwide are rushing to integrate artificial intelligence (AI) technology into urban management
Because more accurate recognition results can be obtained when a frame is closer to the camera, the red virtual judgment line is used in the present study
Where TP represents the positive samples that are judged as positive, FP represents the negative samples that are judged as negative, FN represents the positive samples that are judged as negative, TN represents the negative samples that are judged as negative, N represents level type, and AP represents the average precision value on a precision–recall curve

Summary

Introduction

Governments worldwide are rushing to integrate artificial intelligence (AI) technology into urban management. Many ALPR systems incorporate information technology in urban settings; these technologies include interval speed tracking [1,2], unmanned parking lot management [3], and roadside illegal traffic enforcement [4]. Existing ALPR systems can be divided into two main categories, namely multi- and single-stage methods. Most existing ALPR systems employ the multi-stage method, which is divided into three main stages as follows: (1) location of license plate, (2) character segmentation, and (3) identification of license plate characters [5]. A single-stage method uses deep learning technology to locate and recognize a license plate. In a traditional ALPR system, the multi-stage method uses techniques such as binarization [6], edge detection, character detection, texture detection, color detection, template matching technology, and feature extraction [7]. The aforementioned techniques are affected by environmental factors such as license plate tilt, blurring, and lighting-related changes [8]

Methods

Results

Conclusion