License plate scanners have grown in popularity in parking lots during the past few years. In order to quickly identify license plates, traditional plate recognition devices used in parking lots employ a fixed source of light and shooting angles. For skewed angles, such as license plate images taken with ultra-wide angle or fisheye lenses, deformation of the license plate recognition plate can also be quite severe, impairing the ability of standard license plate recognition systems to identify the plate. Mask RCNN gadget that may be utilised for oblique pictures and various shooting angles. The results of the experiments show that the suggested design will be capable of classifying license plates with bevel angles larger than 0/60. Character recognition using the suggested Mask R-CNN approach has advanced significantly as well. The proposed Mask R-CNN method has also achieved significant progress in character recognition, which is tilted more than 45 degrees as compared to the strategy of employing the YOLOv2 model. Experiment results also suggest that the methodology presented in the open data plate collecting is better than other techniques (known as the AOLP dataset).

翻译：近年来，停车场中的车牌扫描器日益普及。传统停车场使用的车牌识别设备为快速识别车牌，通常采用固定光源和拍摄角度。对于倾斜角度（如使用超广角或鱼眼镜头拍摄的车牌图像），车牌识别板的变形可能相当严重，从而影响标准车牌识别系统的识别能力。本文提出一种可用于倾斜图像及不同拍摄角度的Mask R-CNN装置。实验结果表明，该设计方案能够对倾斜角度大于0/60的车牌进行分类。此外，所提出的Mask R-CNN方法在字符识别方面也取得了显著进展。与采用YOLOv2模型的策略相比，该方法在识别倾斜角度超过45度的字符时表现更优。实验结果还表明，本文提出的方法在开放数据车牌数据集（即AOLP数据集）上优于其他技术。