# Edge-AI-Based Real-Time Automated License Plate Recognition System
###### 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. license plate recognition; deep learning; embedded system; You Only Look Once (YOLO) +
Footnote †: journal: _Sci_  
ALPR systems usually use image binarization or grayscale to locate candidate license plates and characters, after which manual methods are applied for feature extraction and traditional machine learning methods are employed for character classification [7]. With the recent development of deep learning, numerous studies have adopted the convolutional neural network (CNN) for license plate recognition because of its high accuracy with respect to general object detection and recognition [9; 10]. [PERSON] et al. [11] used a series of preprocessing methods (based on morphological operators, Gaussian filtering, edge detection, and geometrical analysis) to locate license plates and characters. Subsequently, they used two different CNNs to: (1) group a set of license plate candidates for each image into a single positive sample; and (2) recognize license plate characters. In this method, a single license plate is processed for each image, and the resulting distortion of a license plate is primarily caused by poor lighting conditions. [PERSON] et al. [12] proposed the use of a CNN for training and fine-tuning each ALPR stage, such that the system remains robust even under dynamic conditions (e.g., different cameras, lighting, and backgrounds); in particular, this strategy allows for a good recognition rate to be obtained in character segmentation and recognition. License plate styles, sorting rules, and compositions of license plate characters differ between countries. When real-time image recognition is applied to traffic, blurred images due to vehicle movement are captured. Generally, public data sets mostly contain static images of parking lots; therefore, they cannot be used for training. Variations in style and weather increase the difficulty of collecting data. [PERSON] et al. [4] used a generative adversarial network to generate multiple license plate images. Their You Only Look Once V2 (YOLOv2) [13] performed well on a generated synthetic data set. To address the challenges posed by changing scenery and differently styled license plates, the recognition accuracy of image preprocessing technology should be improved. In machine vision, which is a character recognition technology, comparisons with color or grayscale images are conducted to enable the binarization method to more easily separate the pixels belonging to license plate characters in an image.