面向铁路货车车号定位的 Faster R-CNN卷积神经网络

为了解决传统算法对于铁路货运列车车号识别准确率不高问题,提出了一种面向铁路货车车号定位的 Faster R-CNN 神 经网络。 通过调整特征提取网络的相关尺寸参数及连接方式增强了最后一层卷积特征图的细节特征。 并采用 k-means++聚类 算法求取车号区域长宽比改进 anchor 尺寸设计,使目标检测框与实际车号区域更加贴合。 实验过程中,采用了数据增广、 dropout 方法提升网络的鲁棒性。 结果显示,改进 Faster R-CNN 网络在铁路货车车号定位精度达到了 93. 15%,召回率 90. 76%, 综合 F1 指标 91. 94%,也说明该方法能够对铁路货车车号准确定位,并为车号识别过程提供可靠的数据支持。

Faster R-CNN convolutional neural network for the location of freight train number

In order to solve the problem of low accuracy of traditional algorithm for train number identification of railway freight trains, Faster R-CNN neural network for train number location of railway freight trains is proposed. The detailed features of the final convolution feature map are enhanced by adjusting the relevant size parameters and connection mode of the feature extraction network. The k-means ++ clustering algorithm is used to calculate the length width ratio of the train number area. The improved anchor size design makes the target detection frame more suitable for the actual train number area. In the experiment, data augmentation and dropout are used to improve the robustness of the network. The results show that the improved Faster R-CNN network has achieved 93. 15% accuracy in the location of railway freight train number, 90. 76% recall rate and 91. 94% comprehensive F1 index. It also shows that this method can accurately locate the railway freight train number and provide reliable data support for the identification process.