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http://dx.doi.org/10.5909/JBE.2020.25.5.776

Proposal for License Plate Recognition Using Synthetic Data and Vehicle Type Recognition System  

Lee, Seungju (Dept. of Media IT Engineering, The Graduate Schol, Seoul National University of Science and Technology)
Park, Gooman (Dept. of Electronic IT Media Engineering, Seoul National University of Science and Technology)
Publication Information
Journal of Broadcast Engineering / v.25, no.5, 2020 , pp. 776-788 More about this Journal
Abstract
In this paper, a vehicle type recognition system using deep learning and a license plate recognition system are proposed. In the existing system, the number plate area extraction through image processing and the character recognition method using DNN were used. These systems have the problem of declining recognition rates as the environment changes. Therefore, the proposed system used the one-stage object detection method YOLO v3, focusing on real-time detection and decreasing accuracy due to environmental changes, enabling real-time vehicle type and license plate character recognition with one RGB camera. Training data consists of actual data for vehicle type recognition and license plate area detection, and synthetic data for license plate character recognition. The accuracy of each module was 96.39% for detection of car model, 99.94% for detection of license plates, and 79.06% for recognition of license plates. In addition, accuracy was measured using YOLO v3 tiny, a lightweight network of YOLO v3.
Keywords
YOLO; Synthetic Data Generation; Object Detection; Text Recognition;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
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