Artificial intelligence or AI is a hot topic these days and an oft-used term in nearly every industry. Automation, quality inspection and manufacturing are no exception. Rather, these are a major proving ground for development, experimentation, and implementation of AI for industrial applications. In many instances however, AI is a bit of a catchall term, often referring to the application of some level of computer-assisted decision-making. When we unpack it, the more relevant pieces that people often speak of are machine learning, deep learning, and computer vision – individually different but collectively referred to as AI. The discussion stands to benefit from some untangling to enable an improved level of clarity around what each does, why they matter and the value and benefit that machine learning brings to the industrial automation and quality inspection landscapes.
Put simply, computer vision is the process of interpreting information from images or videos in an automated fashion using computers instead of people. The algorithms and models used in computer vision applications tend to be rigid in that they are tailored to find and identify specific items in a scene – these could be defects, the absence or presence of something, or incorrect characters on a label. The inspection environment needs to remain static as do the items being inspected. Changes in the variables almost always mean that the model must be updated, reconfigured, and redeployed – by humans.