摘要:
编组站内机车车号的识别问题一直制约着本务机车综合管控技术的发展。为了解决这一问题,针对机车车次,机车类型自动识别问题进行研究。改进了基于卷积神经网络LeNet-5的识别算法,并收集了大量机车车次图像素材,通过图像预处理后,使用训练集进行模型训练,形成适用于机车车次识别的网络模型,通过使用python语言与.NET平台实现了机车车次识别系统的设计。实验表明,该方法对机车车号的识别达到了较高的识别水平。目前,车号识别系统已在中国铁路武汉局集团有限公司襄阳北站试验,高清图像素材从车站高清货检系统处获取,识别效果良好,为实现智慧型编组站提供了有力的技术支撑。
Abstract:
The problem of the identification for the locomotive number and types in the marshalling station always restricted the development of the integrated control technology of the locomotive.In order to solve this problem, this article studied the automatic identification of locomotive types and locomotive number, proposed an improved identification algorithm based on convolutional neural network LeNet-5, collected a lot of image materials of locomotive number. After image preprocessing, the training set was used to train the model, and a network model suitable for locomotive number identification was formed. The design of locomotive number identification system was implemented by using Python language and. NET platform. Experiments show that the method has reached a high recognition level for locomotive number identification. At present, the locomotive number identification system has been tested in Xiangyang North Station of Wuhan Railway Administration. The high-definition image materials are obtained from the high-definition cargo inspection system of the station, and the recognition effect is good, which provides strong technical support for the implementation of intelligent marshalling station.
点击查看大图
下载:
