摘要
弓网系统在高速行驶的动车组运行过程中发挥着至关重要的作用,列车在运行过程中,受接触网高度变化、列车运行线路复杂背景等因素影响,给受电弓的准确定位及后续故障检测带来巨大干扰。本文提出了一种基于Haar-like特征分类器的列车受电弓智能定位方法。该方法采用积分图方式来高效计算受电弓监测视频帧下的Haar-like特征,利用Adaboost算法训练受电弓分类器,从而得到级联boosted分类器,以完成受电弓的实时位置检测。上述方法的有效性和泛化性在2段真实的受电弓监控视频流中得到验证,实现了在复杂背景噪声下的受电弓区域的准确有效定位。
The bow network system plays a vital role in the operation of high-speed moving trains.During the train operation,it is affected by the contact network height change,the complex background of train running line and other factors,which bring great interference to the accurate positioning of pantograph and subsequent fault detection.In this paper,an intelligent localization method for train pantographs based on Haar-like feature classifiers is proposed.The method adopts an integral map approach to efficiently calculate the Haar-like features under the pantograph monitoring video frames.The Adaboost algorithm is utilized to train the pantograph classifier,which results in a cascaded boosted classifier to accomplish real-time position detection of the pantograph.The effectiveness and generalization of the above method is verified in 2 real pantograph monitoring video streams,which achieves accurate and effective localization of the pantograph region under complex background noise.
作者
邱岳
吴连军
Qiu Yue;Wu Lianjun(CRCC Industrial Research Institute(Qingdao)Co.,Ltd.,Qingdao,China)
出处
《科学技术创新》
2023年第26期47-51,共5页
Scientific and Technological Innovation
