论文标题
Yolo和Mask R-CNN用于车辆板识别
YOLO and Mask R-CNN for Vehicle Number Plate Identification
论文作者
论文摘要
在过去的几年中，车牌扫描仪在停车场的流行增长。为了快速识别车牌，停车场中使用的传统板识别设备采用固定的光源和射击角度来源。对于偏斜的角度，例如用超宽角或鱼眼镜镜拍摄的车牌图像，车牌识别板的变形也可能很严重，从而损害了标准车牌识别系统识别板的能力。蒙版RCNN小工具可以用于倾斜的图片和各种拍摄角度。实验的结果表明，建议的设计将能够对大于0/60的斜角角度进行分类。使用建议的蒙版R-CNN方法的角色识别也已显着提高。所提出的面膜R-CNN方法还取得了角色识别的重大进展，与使用Yolov2模型的策略相比，该方法的倾斜度超过45度。实验结果还表明，在开放数据板收集中介绍的方法比其他技术（称为AOLP数据集）更好。
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).
