arXiv 2604.087412026-04-09cs.CV

LPLCv2：细粒度车牌可读性分类扩展数据集

LPLCv2: An Expanded Dataset for Fine-Grained License Plate Legibility Classification

1 0.15mm Federal University of Paran\'a, Curitiba, Brazil；2 0.15mm Department of Technological Development；3 0.15mm Pontifical Catholic University of Paran\'a, Curitiba, Brazil

Lucas Wojcik, Eduardo A. F. Machoski, Eduil Nascimento, Rayson Laroca, David Menotti

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AI总结：

本文扩展了细粒度车牌可读性分类基准数据集，引入更丰富的标注和改进的训练方法，使基线模型在测试集上达到89.5%的F1分数，超越了先前的最先进水平。

摘要：

现代自动车牌识别（ALPR）系统在受控、明确的场景中表现出色。然而，大规模现实应用仍面临低质量成像设备、压缩伪影和次优相机安装的挑战。通过专门的基准测试，识别不可读车牌已成为可能；然而，其影响受限于其小尺寸和标注错误。在本文中，我们扩展了原始基准，使其规模扩大三倍，增加了两天的拍摄，修订了标注并引入了新标签。车牌级别的标注包括边界框、文本和可读性等级，而车辆级别的标注包括品牌、型号、类型和颜色。图像级别的标注包括相机身份、拍摄条件（例如雨和故障相机）、拍摄时间以及日期ID。我们提出了一种新的训练过程，包含基于指数移动平均的损失函数和改进的学习率调度器，解决了测试中的常见错误。这些改进使基线模型在测试集上达到89.5%的F1分数，显著超越了先前的最先进水平。我们进一步引入了一种新的协议，以明确解决训练和评估分割之间的相机污染问题，结果显示影响较小。数据集和代码可在https://github.com/lmlwojcik/LPLCv2-Dataset公开获取。

英文摘要：

Modern Automatic License Plate Recognition (ALPR) systems achieve outstanding performance in controlled, well-defined scenarios. However, large-scale real-world usage remains challenging due to low-quality imaging devices, compression artifacts, and suboptimal camera installation. Identifying illegible license plates (LPs) has recently become feasible through a dedicated benchmark; however, its impact has been limited by its small size and annotation errors. In this work, we expand the original benchmark to over three times the size with two extra capture days, revise its annotations and introduce novel labels. LP-level annotations include bounding boxes, text, and legibility level, while vehicle-level annotations comprise make, model, type, and color. Image-level annotations feature camera identity, capture conditions (e.g., rain and faulty cameras), acquisition time, and day ID. We present a novel training procedure featuring an Exponential Moving Average-based loss function and a refined learning rate scheduler, addressing common mistakes in testing. These improvements enable a baseline model to achieve an 89.5% F1-score on the test set, considerably surpassing the previous state of the art. We further introduce a novel protocol to explicitly addresses camera contamination between training and evaluation splits, where results show a small impact. Dataset and code are publicly available at https://github.com/lmlwojcik/LPLCv2-Dataset.

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