RESEARCH YOLOv8 AND YOLO-NAS VERSIONS  IN LICENSE PLATE DETECTION

Đặng Thị Dung; Hà Lê Ngọc Dung; Trương Lê Chương; Thái Chí Hào; Trần Văn Phúc

doi:10.34238/tnu-jst.10336

RESEARCH YOLOv8 AND YOLO-NAS VERSIONS IN LICENSE PLATE DETECTION

About this article

Received: 09/05/24 Revised: 10/06/24 Published: 11/06/24

Authors

1. Dang Thi Dung , Can Tho University of Engineering - Technology
2. Ha Le Ngoc Dung, Can Tho University of Engineering - Technology
3. Truong Le Chuong, Can Tho University of Engineering - Technology
4. Thai Chi Hao, Can Tho University of Engineering - Technology
5. Tran Van Phuc, Can Tho University of Engineering - Technology

Abstract

Recently, license plate recognition systems have been an important part of many traffic management and security systems such as automatic speed control, stolen vehicle tracking, automatic toll management, and control of vehicles entering and exiting bus station areas, schools, hospitals, etc. During the research process, we compared versions of YOLOv8 and YOLO-NAS based on the criteria of Accuracy, Precision, Recall, and F1 score to evaluate the most suitable models for vehicle license plate recognition in Vietnam under different environmental conditions. This review provides perspective for developers or last users to choose the most suitable technique for their application. The results show that for applications with good infrastructure and high accuracy requirements, YOLO-NAS-S is a suitable model with an Accuracy of 83.92%, Precision of 0.9125; Recall is 0.9125, and F1 score is 0.9125. For less developed infrastructure and speed requirements, YOLOv8n can be used with a smaller number of parameters but the accuracy is acceptable, Accuracy is 81.4%; Precision is 0.9625; Recall is 0.8415, and F1 score is 0.8979.

Keywords

YOLOv8; YOLO-NAS; Vehicle License Plate Detection; Machine Learning; Deep Learning

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DOI: https://doi.org/10.34238/tnu-jst.10336

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