Abstract
Many real-life machine and computer vision applications are focusing on object detection and recognition. In recent years, deep learning-based approaches gained increasing interest due to their high accuracy levels. License Plate (LP) detection and classification have been studied extensively over the last decades. However, more accurate and language-independent approaches are still required. This paper presents a new approach to detect LPs and recognize their country, language, and layout. Furthermore, a new LP dataset for both multi-national and multi-language detection, with either one-line or two-line layouts is presented. The YOLOv2 detector with ResNet feature extraction core was utilized for LP detection, and a new low complexity convolutional neural network architecture was proposed to classify LPs. Results show that the proposed approach achieves an average detection precision of 99.57%, whereas the country, language, and layout classification accuracy is 99.33%.
Highlights
Object detection and classification has attracted a lot of research the recent years, with the advancements in vision technology, computer technology, and deep learning algorithms [1]
Α simple Convolutional Neural Network (CNN) was designed for License Plate (LP) classification, and its accuracy is compared to VGG [13]
Any detected LP bounding box having an overlap greater than intersection of union (IOU)=0.5 with the ground truth bounding box is considered as a correct detection
Summary
Object detection and classification has attracted a lot of research the recent years, with the advancements in vision technology, computer technology, and deep learning algorithms [1]. A new approach was proposed in [29], referred as YOLO-L, where the prospective number and size of LP candidate boxes are selected using “k-means++” clustering with a modified YOLOv2 model and pre-identification to distinguish LPs from similar objects This method achieved a precision of 98.86%. Multi-national LPs from USA, Europe (EU), Turkey (TR), UAE and Kingdom of Saudi Arabia (KSA) are targeted, using YOLOv2 detector with ResNet feature extraction for LPD For this purpose, a new dataset, named as LPDC2020, was constructed and presented. The proposed approach aims to close the gap in multi-national, multi-language and multi-layout LP detection problem, by utilizing a single unified system, and to the best of our knowledge it is the first and only study incorporating LPs from North and South America, Europe, and Middle East (TR, UAE and KSA)
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