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License plate detection based on fully convolutional networks

机译:基于全卷积网络的车牌检测

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Fully convolutional networks (FCNs) have shown outstanding performance in image semantic segmentation, which is the key work in license plate detection (LPD). An FCN architecture for LPD is presented. First, a multiscale hierarchical network structure is used to combine multiscale and multilevel features produced by FCN. Then, an enhanced loss structure that contains three loss layers is defined to emphasize the license plates in images. Finally, the FCN generates prediction maps that directly show the location of license plates. Experiments show that our approach is more accurate than many state-of-the-art methods. (C) 2017 SPIE and IS&T
机译:全卷积网络(FCN)在图像语义分割中表现出出色的性能,这是车牌检测(LPD)的关键工作。提出了用于LPD的FCN体系结构。首先,使用多尺度分层网络结构来组合FCN产生的多尺度和多层次特征。然后,定义了包含三个损失层的增强损失结构,以强调图像中的车牌。最后,FCN生成直接显示车牌位置的预测图。实验表明,我们的方法比许多最新方法更准确。 (C)2017 SPIE和IS&T

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