CCPD

CCPD, a large and comprehensive LP dataset. All images are taken manually by workers of a roadside parking management company and are annotated carefully. To our best knowledge, CCPD is the largest publicly available LP dataset to date with over 250k unique car images, and the only one provides vertices location annotations. With CCPD, we present a novel network model which can predict the bounding box and recognize the corresponding LP number simultaneously with high speed and accuracy. Through comparative experiments, we demonstrate our model outperforms current object detection and recognition approaches in both accuracy and speed. In real-world applications, our model recognizes LP numbers directly from relatively high-resolution images at over 61 fps and 98.5% accuracy. The split file is available under 'split/' folder. Images in CCPD-Base is split to train/val set. Sub-datasets (CCPD-DB, CCPD-Blur, CCPD-FN, CCPD-Rotate, CCPD-Tilt, CCPD-Challenge) in CCPD are exploited for test. As each image in CCPD contains only a single license plate (LP). Therefore, we do not consider recall and concerntrate on precision. Detectors are allowed to predict only one bounding box for each image.

搜集汇总

背景与挑战

背景概述

CCPD是一个大型车牌数据集，包含超过25万张手动拍摄和标注的独特汽车图像，是目前最大的公开车牌数据集，且唯一提供顶点位置标注。该数据集用于训练和测试一个高效网络模型，能同时预测边界框和识别车牌号码，在实时应用中达到超过61 fps和98.5%的准确率。数据集分为训练/验证集和多个测试子集，专注于检测精度和字符级识别评估。