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%.
翻译:对技术系统或机器机械部件的功能进行智能监测是完全自动生产过程的必备条件。虽然深入学习一直在推进,可以实时探测物体和其他任务,但对专门设计的革命神经网络在发现缺陷和工业物体识别方面的效力调查甚少。在特别研究中,我们使用了六套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部件,目的是开发一种专用模式识别模型。我们以虚拟几何组网络最近的成功为动力,提出了一个修改版本,称为多路德VGG19,允许更多本地和全球地物提取,而额外地物则通过凝固结合。实验证实了MVGG19相对于传统的VGG19的有效性。具体地说,六套图像数据集中,有五套实现了最高分类绩效,而平均分类改进率为6.95%。