基于卷积神经网络的接触网支柱号识别方法研究

接触网支柱号是铁路供电部门现场运营维护的重要定位参数。基于卷积神经网络的接触网支柱号自动识别方法结合接触网图像的实际特点,对视频图像进行了归一化图像预处理,并对实际的支柱图片进行了卷积神经网络的训练,在支柱号识别确定的过程中考虑了接触网支柱号的分布特点,提高了支柱号识别的准确性。利用实际线路数据进行测试,取得了较好的识别精度和较快的识别速度。通过实验验证,该方法能够辅助铁路基础设施检测系统中缺陷的定位,指导现场运营维修。

Catenary pillar number recognition method based on convolutional neural network

The catenary pillar numberis an important positioning parameter for on-site operation and maintenance of railway power supply department. Based on convolution neural network, the automatic recognition method of catenary pillar number was combined with the actual characteristics of catenary image. The video image was preprocessed by normalization, and the actual pillar image was trained by convolution neural network. The distribution characteristics of catenary pillar number were considered in the process of the identifying. It improves the accuracy of pillar number recognition.The actual railway line data were used to test, and better recognition accuracy and faster recognition speed were obtained. The experimental results show that this method can assist the defect location in the railway infrastructure inspection system and guide on-site operation and maintenance.