Journal
IMAGE AND VISION COMPUTING
Volume 87, Issue -, Pages 47-56Publisher
ELSEVIER
DOI: 10.1016/j.imavis.2019.04.007
Keywords
Automatic License Plate Recognition; Deep learning; YOLO network
Categories
Funding
- Ministry of Science and Technology, Taiwan [MOST-107-2221-E-324-018-MY2, MOST-106-2221-E-324-025]
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Automatic License Plate Recognition (ALPR) is an important research topic in the intelligent transportation system and image recognition fields. In this work, we address the problem of car license plate detection using a You Only Look Once (YOLO)-darknet deep learning framework. In this paper, we use YOLO's 7 convolutional layers to detect a single class. The detection method is a sliding-window process. The object is to recognize Taiwan's car license plates. We use an AOLP dataset which contained 6 digit car license plates. The sliding window detects each digit of the license plate, and each window is then detected by a single YOLO framework. The system achieves approximately 98.22% accuracy on license plate detection and 78% accuracy on license plate recognition. The system executes a single detection recognition phase, which needs around 800 ms to 1 s for each input image. The system is also tested with different condition complexities, such as rainy background, darkness and dimness, and different hues and saturation of images. (C) 2019 Elsevier B.V. All rights reserved.
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