  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.
