A Deep Learning Model of Dual-Stage License Plate Recognition Applicable to the Data Processing Industry

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.

Studies Advanced in License Plate Recognition

“…Texture-based methods rely on the detection of characters on license plates take for a means of identifying them [9]. The approach is based on the observation that there is a considerable color contrast between the characters and the background of the plate, resulting in frequent color changes.…”

Brain Tumor Segmentation using Automatic 3D Multi-channel Feature Selection Convolutional Neural Network

“…Inevitably, deep neural networks incur high computational costs in both training and testing phases. This is one of the most important reasons that restrict its practical application in consumer electronics [13,14]. Therefore, seeking an effective and lightweight 3D CNN brain tumor segmentation model has great practical application value, which will become the future development trend in the field of brain tumor segmentation.…”