论文标题
半监督的标准化流与Yolo（Yolonf）的高级组合，以检测和识别车牌
An advanced combination of semi-supervised Normalizing Flow & Yolo (YoloNF) to detect and recognize vehicle license plates
论文作者
论文摘要
由于多个实际应用，全自动车牌识别（ALPR）一直是一个经常研究的主题。但是，在实际情况下，许多当前的解决方案仍然不够强大，通常取决于许多限制。本文提出了一个基于最先进的Yolo对象检测器并标准化流量的强大而有效的ALPR系统。该模型使用两种新策略。首先，使用Yolo的两阶段网络和基于归一化流的模型进行标准化以检测许可板（LP）并识别具有数字和阿拉伯字符的LP。其次，实施了多尺度图像转换，以解决Yolo裁剪LP检测问题的问题，包括明显的背景噪声。此外，在具有现实情况的新数据集中进行了广泛的实验，我们引入了从摩洛哥板收集的更大的公共注释数据集。我们证明我们提出的模型可以在没有单个或多个字符的少数样本上学习。该数据集也将公开使用，以鼓励对板检测和识别进行进一步的研究和研究。
Fully Automatic License Plate Recognition (ALPR) has been a frequent research topic due to several practical applications. However, many of the current solutions are still not robust enough in real situations, commonly depending on many constraints. This paper presents a robust and efficient ALPR system based on the state-of-the-art YOLO object detector and Normalizing flows. The model uses two new strategies. Firstly, a two-stage network using YOLO and a normalization flow-based model for normalization to detect Licenses Plates (LP) and recognize the LP with numbers and Arabic characters. Secondly, Multi-scale image transformations are implemented to provide a solution to the problem of the YOLO cropped LP detection including significant background noise. Furthermore, extensive experiments are led on a new dataset with realistic scenarios, we introduce a larger public annotated dataset collected from Moroccan plates. We demonstrate that our proposed model can learn on a small number of samples free of single or multiple characters. The dataset will also be made publicly available to encourage further studies and research on plate detection and recognition.
