License plates are the primary source of vehicle identification data used in a wide range of applications including law enforcement, electronic tolling, and access control amongst others. License plate detection (LPD) is a critical process in automatic license plate recognition (ALPR) that reduces complexity by delimiting the search space for subsequent ALPR stages. It is complicated by unfavourable factors including environmental conditions, occlusion, and license plate variation. As such, it requires training models on substantial volumes of relevant images per use case. In 2018, the new Mercosur standard came in to effect in four South American countries. Access to large volumes of actual Mercosur license plates with sufficient presentation variety is a significant challenge for training supervised models for LPD, thereby adversely impacting the efficacy of ALPR in Mercosur countries. This paper presents a novel license plate embedding methodology for generating large volumes of accurate Mercosur license plate images sufficient for training supervised LPD. We validate this methodology with a deep learning-based ALPR using a convolutional neural network trained exclusively with synthetic data and tested with real parking lot and traffic camera images. Experiment results achieve detection accuracy of 95% and an average running time of 40 ms.

车牌是车辆识别数据的主要来源，广泛用于包括执法，电子收费和访问控制在内的各种应用中。车牌检测（LPD）是自动车牌识别（ALPR）中的关键过程，它通过为后续ALPR阶段划定搜索空间来降低复杂性。由于不利的因素（包括环境条件，遮挡和车牌变化）而使其复杂化。因此，它需要针对每个用例在大量相关图像上进行训练的模型。2018年，新的南方共同市场标准在四个南美国家生效。对于训练LPD的受监督模型而言，要获得大量具有足够演示格式的实际Mercosur车牌，是一项重大挑战，从而对南共市国家的ALPR效力产生不利影响。本文提出了一种新颖的车牌嵌入方法，可生成大量准确的Mercosur车牌图像，足以训练有监督的LPD。我们使用基于卷积神经网络的基于深度学习的ALPR验证了这种方法，该卷积神经网络专门训练了合成数据并经过了实际停车场和交通摄像头图像的测试。实验结果实现了95％的检测精度和40 ms的平均运行时间。我们使用基于卷积神经网络的基于深度学习的ALPR验证了这种方法，该卷积神经网络专门训练了合成数据并经过了实际停车场和交通摄像头图像的测试。实验结果实现了95％的检测精度和40 ms的平均运行时间。我们使用基于卷积神经网络的基于深度学习的ALPR验证了这种方法，该卷积神经网络专门训练了合成数据并经过了实际停车场和交通摄像头图像的测试。实验结果实现了95％的检测精度和40 ms的平均运行时间。