Abstract
Automatic License Plate Recognition (ALPR) is a widely used technology. However, due to the influence of complex environmental factors, recognition accuracy and speed of license plate recognition have been challenged and expected. Aiming to construct a sufficiently robust license plate recognition model, this study adopted multitask learning in the license plate detection stage, used the convolutional neural networks of single-stage detection, RetinaFace, and MobileNet, as approaches to license plate location, and completed the license plate sampling through the calculation of license plate skew correction. In the license plate character recognition stage, the Convolutional Recurrent Neural Network (CRNN) integrated with the loss function of the CTC model was employed as a segmentation-free and highly robust method of license plate character recognition. In this study, after the license plate recognition model, DLPR, trained the PVLP dataset of vehicle images provided by company A in Taiwan’s data processing industry, it performed tests on the PVLP dataset, indicating that its precision was 98.60%, recognition accuracy was 97.56%, and recognition speed was FPS > 21. In addition, according to the tests on the public AOLP dataset of Taiwan’s vehicles, its recognition accuracy was 97.70% and recognition speed was FPS > 62. Therefore, not only can the DLPR model be applied to the license plate recognition of real-time image streams in the future, but also it can assist the data processing industry in enhancing the accuracy of license plate recognition in photos of traffic violations and the performance of traffic service operations.
Highlights
Introduction e research on Automatic LicensePlate Recognition (ALPR) has been done for more than 20 years [1]
In terms of research contribution, after the Dual-stage License Plate Recognition Model (DLPR) proposed by this study used the Private Vehicle License Plate (PVLP) dataset provided by company A in Taiwan’s data processing industry to train license plate location and license plate character recognition, the final accuracy of license plate recognition could reach 97.56% in the PVLP dataset, and frame per second (FPS) > 21
This study performed tests on the Application-Oriented License Plate (AOLP) dataset and compared it with its related research; the results indicated that the license plate accuracy for all of the AOLP subdatasets gathered in the DLPR model was the best, with an average of 97.70%, and FPS could reach 62.93
Summary
Introduction e research on Automatic LicensePlate Recognition (ALPR) has been done for more than 20 years [1]. ALPR technology has made considerable progress and has been widely used in different application fields and industries, such as automatic traffic violation detection [2, 3]. E processing procedure is as follows: first, obtain the data of traffic violation cases from various reporting units; submit the data to the filing center to import traffic violation photos and citations into the filing system and to complete registration and monitoring after confirming the car registration information with the Motor Vehicles Office; last, pass the data on to the printing center to complete printing, postal delivery, and transferred mailing data packaging. While importing the traffic violation cases into the filing system, the filer can learn the car registration information, date, location, speed limit, driving speed, and so on from the photo and citation. The filer must carefully confirm the data one by one and import them. is process is all manually performed, which is cumbersome and likely to cause business losses to the company when the imported data are incorrect
Sign-in/Register to view highlights & summary 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 callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
