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Railway freight train number detection method based on deep learning

机译:基于深度学习的铁路货运列号检测方法

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In view of the complicated environmrntal background of freight trains loading stations in coal mines, the accuracy of train number positioning is easily affected by various influences. This paper aims to optimize the EAST natural text detection model and apply it to the train number positioning of railway freight trains. The improved ResNet50 network is used to extract the car number feature, and the BLSTM is added to the Feature that is about to be deconvolved in each layer. At the same time, when the polygon is made, the shrink distance of 0.3 times is changed to 0.1 times. Experiments show that the vehicle number area can be accurately detected through optimization.
机译:鉴于运费的复杂环保背景装载煤矿中的装载站,火车号码定位的准确性很容易受到各种影响的影响。本文旨在优化东部自然文本检测模型,并将其应用于火车货运列车的火车编号定位。改进的RENET50网络用于提取CAR NUMBER功能,并且BLSTM被添加到即将在每层中被解码的特征。同时,当多边形进行时,将0.3倍的收缩距离变为0.1倍。实验表明,通过优化可以精确地检测车辆编号区域。

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