论文标题
带有Yolov3-CNN的自动数板识别（ANPR）
Automatic Number Plate Recognition (ANPR) with YOLOv3-CNN
论文作者
论文摘要
我们提出了一条Yolov3-CNN管道，用于检测车辆，数板的隔离以及最终公认字符的本地存储。在各种图像校正方案下进行车辆识别，以确定环境因素的影响（感知角度，光度，运动启动和多行自定义字体等）。训练了Yolov3对象检测模型，以从交通图像的数据集中识别车辆。训练了第二个Yolov3层，以识别车辆图像的数字板。基于校正方案，对实时数据进行隔离并验证各个字符，以计算这种方法的准确性。虽然直接识别的角色被准确识别，但受环境因素影响的一些数字降低了准确性。我们总结了各种环境因素与实时数据的结果，并产生管道模型的总体准确性。
We present a YOLOv3-CNN pipeline for detecting vehicles, segregation of number plates, and local storage of final recognized characters. Vehicle identification is performed under various image correction schemes to determine the effect of environmental factors (angle of perception, luminosity, motion-blurring, and multi-line custom font etc.). A YOLOv3 object detection model was trained to identify vehicles from a dataset of traffic images. A second YOLOv3 layer was trained to identify number plates from vehicle images. Based upon correction schemes, individual characters were segregated and verified against real-time data to calculate accuracy of this approach. While characters under direct view were recognized accurately, some numberplates affected by environmental factors had reduced levels of accuracy. We summarize the results under various environmental factors against real-time data and produce an overall accuracy of the pipeline model.
