基于卷积神经网络的车牌定位算法

Convolutional-Neural-Network Based License Plate Location Algorithm

针对当前车牌识别系统在存在复杂环境以及车牌倾斜的情况下无法精确定位的问题,提出一种基于卷积神经网络的端到端车牌精确定位算法,从而精确计算车牌的坐标。通过Faster R-CNN对输入车辆图片中的信息进行处理,提取候选区域的特征映射,利用特征映射计算车牌的精确坐标。实验结果表明本文算法在OpenITS数据库的功能评测数据库中的平均识别准确率为99%,在性能评测数据库中的平均识别准确率为85%。

The existing license plate recognition system is unable to locate accurately in the cases of complex background and license plate tilting.To solve this issue,this study proposes an end-to-end license plate location algorithm based on a convolutional neural network to accurately calculate the license plate coordinates.The information is extracted based on the input vehicle picture using Faster R-CNN to obtain the feature mapping of the candidate area.Further,the license plate coordinates are precisely obtained using feature mapping. The experimental results denote that the recognition accuracy of the proposed algorithm is 99% with respect to the functional assessment database of the OpenITS database and 85% with respect to the performance evaluation database.