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改进的 VGG 网络的二极管玻壳图像缺陷检测

  

  1. (西南科技大学信息工程学院，四川 绵阳 621000)
  • 出版日期:2019-12-31 发布日期:2020-01-20
  • 基金资助:
    国家自然科学基金项目(11602292)

Improved VGG Neural Network Applied to Defect Detection of   Diode Glass Bulb Image

  1. (Information Engineering Institute, Southwest University of Science and Technology, Mianyang Sichuan 621000, China)
  • Online:2019-12-31 Published:2020-01-20

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摘要： 针对于目前二极管玻壳缺陷检测中大多采用人工特征提取，识别准确率不高的问 题，提出一种改进的 VGG 网络的二极管玻壳图像缺陷检测方法。首先对玻壳图像进行预处理， 同时利用原始大样本数据集对卷积神经网络结构 VGG-19 模型进行预训练得到预训练模型，然 后通过迁移学习的方法将预训练模型中的部分卷积、池化等层权重参数迁移到改进网络模型的 固定层中，非固定层用于模型改进，并将网络的全连接层结构重新进行超参数设置和优化，最 后使用预处理后的玻壳图像数据集对改进模型进行训练，得到非固定卷积层和新的全连接层的 参数和权重。在二极管玻壳数据集进行测试，实验结果表明，该方法能有效提高二极管玻壳图 像分类识别准确率，达到了 98.3%。

关键词: 玻壳缺陷检测, VGG-19 模型, 迁移学习, 图像分类, 准确率

Abstract: In response to the solution of the problem that the image defects of diode glass bulb are mostly detected by manual feature extraction and the recognition is of low accuracy, an improved VGG network for diode glass bulb image defect detection is proposed. Firstly, the glass bulb image is preprocessed. At the same time, the VGG-19 model of convolution neural network structure is pre-trained by the original large sample data set to obtain the pre-training model. Then, part of the weight parameters of the pre-training model, such as convolution and pooling, are transferred to the fixed layer of the improved network model by the method of transfer learning. In this model, the non-fixed layer is used for improvement and the full-connection layer structure of the network is re-set and optimized. Finally, the improved model is trained with the preprocessed image data set of the glass bulb, and the parameters and weights of the non-fixed convolution layer and the new full-connection layer are obtained. The results of the experiment on the data set of the diode glass bulb show that the method can effectively improve the accuracy of the classification and recognition of the diode glass bulb defect detection and the accuracy rate can reach 98.3%.

Key words: defect detection of diode glass bulb image, VGG-19 model, transfer learning, image classification, accuracy rate 
 
 defect detection of diode glass bulb image, VGG-19 model, transfer learning, image classification, accuracy rate