An embedded automatic license plate recognition system using deep learning

A system to automatically recognize vehicle license plates is a growing need to improve safety and traffic control, specifically in major urban centers. However, the license plate recognition task is generally computationally intensive, where the entire input image frame is scanned, the found plates are segmented, and character recognition is then performed for each segmented character. This paper presents a methodology for engineering a system to detect and recognize Brazilian license plates using convolutional neural networks (CNN) that is suitable for embedded systems. The resulting system detects license plates in the captured image using Tiny YOLOv3 architecture and identifies its characters using a second convolutional network trained on synthetic images and fine-tuned with real license plate images. The proposed architecture has demonstrated to be robust to angle, lightning, and noise variations while requiring a single forward pass for each network, therefore allowing faster processing compared to other deep learning approaches. Our methodology was validated using real license plate images under different environmental conditions reached a detection rate of 99.37% and an overall recognition rate of 98.43% while showing an average time of 2.70 s to process 1024×768\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1024 \times 768$$\end{document} images with a single license plate in a Raspberry Pi3 (ARM Cortex-A53 CPU). To improve the recognition accuracy, an ensemble of CNN models was tested instead of a single CNN model, which resulted in an increase in the average processing time to 4.88 s for each image while increasing the recognition rate to 99.53%. Finally, we discuss the impact of using an ensemble of CNNs considering the accuracy-performance trade-off when engineering embedded systems for license plate recognition.