Abstract
Automatic License Plate Recognition (ALPR) is one of the applications that hugely benefited from Convolutional Neural Network (CNN) processing which has become the mainstream processing method for complex data. Many ALPR research proposed new CNN model designs and post-processing methods with various levels of performances in ALPR. However, good performing models such as YOLOv3 and SSD in more general object detection and recognition tasks could be effectively transferred to the license plate detection application with a small effort in model tuning. This paper focuses on the design of experiment (DOE) of training parameters in transferring YOLOv3 model design and optimising the training specifically for license plate detection tasks. The parameters are categorised to reduce the DOE run requirements while gaining insights on the YOLOv3 parameter interactions other than seeking optimised train settings. The result shows that the DOE effectively improve the YOLOv3 model to fit the vehicle license plate detection task.
Highlights
Automatic License Plate Recognition (ALPR) has been an active field of research in computer vision applications
Much ALPR research focuses on custom Convolutional Neural Network (CNN) models or post-processing methods to tackle different ALPR problems by the format or geo-specific conditions of license plate (LP)
ALPR can be classified into LP detection and character recognition, each with its application implementation challenges
Summary
Automatic License Plate Recognition (ALPR) has been an active field of research in computer vision applications. YOLOv2 algorithm with modified ResNet CNN was proposed by [19] to localise and detect the nature of multi-national LP (country, size, and languages but did not work on recognising the characters on LPs), achieving 99.57% detection precision. The importance of clean data in the CNN application could not be ignored when [11] combined traditional image processing techniques to filter out unnecessary noises and used CNN at the final stage of car plate recognition, achieving 99.6% accuracy. Stage two of ALPR, i.e. character recognition, will not be part of the research for the time being because LP labels are geo-specific and highly dependent on dataset labelling and algorithms. Some parameter values are limited to the original example datasets and not strictly tied to the YOLOv3 algorithm
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
