论文标题
部分可观测时空混沌系统的无模型预测
Exploration of an End-to-End Automatic Number-plate Recognition neural network for Indian datasets
论文作者
论文摘要
印度车辆板在尺寸，字体，脚本和形状方面的种类繁多。因此，自动数板识别（ANPR）解决方案的开发是具有挑战性的，需要一个多样化的数据集作为示例集合。但是，缺少印度情景的全面数据集，从而阻碍了在公共可用和可重现的ANPR解决方案方面的进展。许多国家已经投入了努力，为中国开发全面的ANPR数据集，例如中国城市停车数据集（CCPD）和面向应用程序的车牌（AOLP）数据集为我们提供。在这项工作中，我们发布了一个扩展的数据集，该数据集目前由1.5K图像和可扩展且可重复的程序组成，以增强该数据集以开发为印度条件开发ANPR解决方案。我们利用此数据集探索了印度场景的端到端（E2E）ANPR架构，该架构最初是针对基于CCPD数据集的中国车辆板识别而提出的。当我们为数据集定制架构时，我们遇到了见解，我们在本文中进行了讨论。我们报告了CCPD作者提供的模型直接可重复使用性的障碍，因为印度数字板的极端多样性以及相对于CCPD数据集的分布差异。在将印度数据集的特性与中国数据集对齐后，在LP检测中观察到了42.86％的提高。在这项工作中，我们还将E2E数板检测模型的性能与Yolov5模型进行了比较，并在可可数据集上进行了预训练，并在印度车辆图像上进行了微调。鉴于用于微调检测模块和Yolov5的数量印度车辆图像是相同的，我们得出的结论是，基于可可数据集而不是CCPD数据集为印度条件开发ANPR解决方案更有效。
Indian vehicle number plates have wide variety in terms of size, font, script and shape. Development of Automatic Number Plate Recognition (ANPR) solutions is therefore challenging, necessitating a diverse dataset to serve as a collection of examples. However, a comprehensive dataset of Indian scenario is missing, thereby, hampering the progress towards publicly available and reproducible ANPR solutions. Many countries have invested efforts to develop comprehensive ANPR datasets like Chinese City Parking Dataset (CCPD) for China and Application-oriented License Plate (AOLP) dataset for US. In this work, we release an expanding dataset presently consisting of 1.5k images and a scalable and reproducible procedure of enhancing this dataset towards development of ANPR solution for Indian conditions. We have leveraged this dataset to explore an End-to-End (E2E) ANPR architecture for Indian scenario which was originally proposed for Chinese Vehicle number-plate recognition based on the CCPD dataset. As we customized the architecture for our dataset, we came across insights, which we have discussed in this paper. We report the hindrances in direct reusability of the model provided by the authors of CCPD because of the extreme diversity in Indian number plates and differences in distribution with respect to the CCPD dataset. An improvement of 42.86% was observed in LP detection after aligning the characteristics of Indian dataset with Chinese dataset. In this work, we have also compared the performance of the E2E number-plate detection model with YOLOv5 model, pre-trained on COCO dataset and fine-tuned on Indian vehicle images. Given that the number Indian vehicle images used for fine-tuning the detection module and yolov5 were same, we concluded that it is more sample efficient to develop an ANPR solution for Indian conditions based on COCO dataset rather than CCPD dataset.
