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Label number Recognition Based on Convolutional Neural Networks in industrial products

机译:基于卷积神经网络的工业产品标签号识别

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Aiming at the identification of a certain kind of industrial black material product, this paper proposes a method based on convolutional neural network (CNN) for digital identification of product labels. The platform of image acquisition is set up first, then the digital region is segmented through image processing algorithm and data set is built on it. Finally, the visual geometry group (VGG16) model of convolutional neural network is used to realize the identification of digital labels. Compared with the nearest neighbor based on local binary patterns histograms (LBPH-NN) algorithm and the support vector machine (SVM) algorithm, the performance of CNN is better comprehensively. This research has a good practical significance in the field of industrial production.
机译:针对某类工业黑色材料产品的识别,提出了一种基于卷积神经网络(CNN)的产品标签数字识别方法。首先建立图像采集平台,然后通过图像处理算法对数字区域进行分割,并在其上建立数据集。最后,利用卷积神经网络的视觉几何群(VGG16)模型实现数字标签的识别。与基于局部二进制模式直方图(LBPH-NN)算法和支持向量机(SVM)算法的最近邻算法相比,CNN的综合性能更好。该研究在工业生产领域具有良好的现实意义。

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