论文标题
工业对象，机器部位和缺陷识别用于使用深度学习的全自动工业监测。多级VGG19的情况
Industrial object, machine part and defect recognition towards fully automated industrial monitoring employing deep learning. The case of multilevel VGG19
论文作者
论文摘要
现代行业需要现代解决方案来监视商品的自动生产。对于全自动生产过程，必须对技术系统或机器机械零件的功能进行智能监视。尽管深度学习一直在进步，允许实时对象检测和其他任务，但几乎没有研究专门设计的卷积神经网络以进行缺陷检测和工业对象识别的有效性。在特定的研究中，我们采用了六个公开可用的工业相关数据集，其中包含缺陷材料和工业工具或发动机零件，旨在开发一种用于模式识别的专业模型。在虚拟几何组（VGG）网络的最新成功中，我们提出了一个称为Multipath VGG19的修改版本，该版本允许更多局部和全局特征提取，而额外的功能则通过串联融合。该实验验证了MVGG19对传统VGG19的有效性。具体而言，在六个图像数据集中的五个中实现了顶级分类性能，而平均分类改进为6.95％。
Modern industry requires modern solutions for monitoring the automatic production of goods. Smart monitoring of the functionality of the mechanical parts of technology systems or machines is mandatory for a fully automatic production process. Although Deep Learning has been advancing, allowing for real-time object detection and other tasks, little has been investigated about the effectiveness of specially designed Convolutional Neural Networks for defect detection and industrial object recognition. In the particular study, we employed six publically available industrial-related datasets containing defect materials and industrial tools or engine parts, aiming to develop a specialized model for pattern recognition. Motivated by the recent success of the Virtual Geometry Group (VGG) network, we propose a modified version of it, called Multipath VGG19, which allows for more local and global feature extraction, while the extra features are fused via concatenation. The experiments verified the effectiveness of MVGG19 over the traditional VGG19. Specifically, top classification performance was achieved in five of the six image datasets, while the average classification improvement was 6.95%.
