将基于 AlexNet 卷积神经网络架构的迁移学习应用于铸件表面缺陷的自动识别

S. Thalagala, C. Walgampaya
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引用次数: 8

摘要

表面缺陷的自动化检测在检测成本和时间方面有利于铸造产品制造商,最终影响整体经营绩效。具有图像分类能力的智能系统作为现代智能制造的重要组成部分,在视觉检测领域得到了广泛的应用。卷积神经网络(cnn)执行的图像分类任务最近显示出比传统机器学习技术显著的性能。特别是在CNN架构发展的早期阶段提出的AlexNet CNN架构,表现出了出色的性能。在本文中,我们研究了基于AlexN et CNN架构的迁移学习在铸件表面缺陷分类中的应用。我们使用了包含泵叶轮铸造表面缺陷图像的数据集来测试性能。我们检查了四个实验方案,其中从预训练模型中获得的知识程度在每个实验中都是不同的。此外,使用简单的网格搜索方法,我们探索了两个关键超参数的最佳总体设置。结果表明,尽管结构简单,但结合迁移学习的AlexN网络可以成功地用于泵叶轮铸件表面缺陷的识别。
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Application of AlexNet convolutional neural network architecture-based transfer learning for automated recognition of casting surface defects
Automated inspection of surface defects is beneficial for casting product manufacturers in terms of inspection cost and time, which ultimately affect overall business performance. Intelligent systems that are capable of image classification are widely applied in visual inspection as a major component of modern smart manufacturing. Image classification tasks performed by Convolutional Neural Networks (CNNs) have recently shown significant performance over the conventional machine learning techniques. Particularly, AlexNet CNN architecture, which was proposed at the early stages of the development of CNN architectures, shows outstanding performance. In this paper, we investigate the application of AlexN et CNN architecture-based transfer learning for the classification of casting surface defects. We used a dataset containing casting surface defect images of a pump impeller for testing the performance. We examined four experimental schemes where the degree of the knowledge obtained from the pre-trained model is varied in each experiment. Furthermore, using a simple grid search method we explored the best overall setting for two crucial hyperparameters. Our results show that despite the simple architecture, AlexN et with transfer learning can be successfully applied for the recognition of casting surface defects of the pump impeller.
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