论文标题
用于图像质量检查运输标签的全球本地特征的融合
Fusion of Global-Local Features for Image Quality Inspection of Shipping Label
论文作者
论文摘要
自动运输地址识别和验证的需求增加了，以处理大量包裹，并节省与误会相关的成本。一项先前的研究提出了一个深度学习系统，该系统根据摄像机图像捕获运输地址和条形码区域认可和验证运输地址。由于系统性能取决于输入图像质量，因此对图像预处理进行了输入图像质量的检查是必需的。在本文中，我们提出了一种结合全球和本地特征的输入图像质量验证方法。在不同特征空间中开发了对象检测和规模不变的特征变换，以从几个独立的卷积神经网络中提取全球和局部特征。运输标签图像的条件通过具有串联的全球和本地功能的完全连接的融合层进行分类。关于实际捕获和生成的图像的实验结果表明，所提出的方法比其他方法更好地实现了性能。这些结果有望通过根据机密条件应用不同的图像预处理步骤来改善运输地址识别和验证系统。
The demands of automated shipping address recognition and verification have increased to handle a large number of packages and to save costs associated with misdelivery. A previous study proposed a deep learning system where the shipping address is recognized and verified based on a camera image capturing the shipping address and barcode area. Because the system performance depends on the input image quality, inspection of input image quality is necessary for image preprocessing. In this paper, we propose an input image quality verification method combining global and local features. Object detection and scale-invariant feature transform in different feature spaces are developed to extract global and local features from several independent convolutional neural networks. The conditions of shipping label images are classified by fully connected fusion layers with concatenated global and local features. The experimental results regarding real captured and generated images show that the proposed method achieves better performance than other methods. These results are expected to improve the shipping address recognition and verification system by applying different image preprocessing steps based on the classified conditions.
