论文标题
钢表面中制造缺陷的一声识别
One-Shot Recognition of Manufacturing Defects in Steel Surfaces
论文作者
论文摘要
质量控制是制造过程中的重要过程，可以使产品缺陷以及满足客户需求。此过程的自动化对于保持高质量以及高生产吞吐量很重要。随着深度学习和计算机视觉技术的最新发展，已经有可能以近乎人类的精度从图像中检测到各种特征。但是，其中许多方法都是数据密集型。在制造地板上培训和部署这种系统可能会变得昂贵且耗时。对大量培训数据的需求是这些方法在现实世界制造系统中适用性的局限性之一。在这项工作中，我们建议应用暹罗卷积神经网络对此类任务进行单次识别。我们的结果表明，如何通过鉴定钢表上的缺陷来将一声学习用于钢的质量控制。此方法可以大大减少培训数据的要求，也可以实时运行。
Quality control is an essential process in manufacturing to make the product defect-free as well as to meet customer needs. The automation of this process is important to maintain high quality along with the high manufacturing throughput. With recent developments in deep learning and computer vision technologies, it has become possible to detect various features from the images with near-human accuracy. However, many of these approaches are data intensive. Training and deployment of such a system on manufacturing floors may become expensive and time-consuming. The need for large amounts of training data is one of the limitations of the applicability of these approaches in real-world manufacturing systems. In this work, we propose the application of a Siamese convolutional neural network to do one-shot recognition for such a task. Our results demonstrate how one-shot learning can be used in quality control of steel by identification of defects on the steel surface. This method can significantly reduce the requirements of training data and can also be run in real-time.
