Images or videos recorded in public areas may contain personal data such as license plates. According to German law, one is not allowed to save the data without permission of the affected people or an immediate anonymization of personal information in the recordings. As asking for and obtaining permission is practically impossible for one thing and then again, manual anonymization time consuming, an automated license plate detection and localization system is developed. For the implementation, a two-stage neural net approach is chosen that hierarchically combines a YOLOv3 model for vehicle detection and another YOLOv3 model for license plate detection. The model is trained using a specifically composed dataset that includes synthesized images, the usage of low-quality or non-annotated datasets as well as data augmentation methods. The license plate detection system is quantitatively and qualitatively evaluated, yielding an average precision (AP) of 98.73% for an intersection over union threshold of 0.3 on the openALPR dataset and showing an outstanding robustness even for rotated, small scaled or partly covered license plates.