Machine vision uses sophisticated hardware and artificial intelligence (AI) to “see” by processing visual inputs, like pictures or videos. For example, it can perform surveillance by analyzing security-cam footage and flagging suspicious activity. It can also help a computer “read” and digitize documents or label objects in photos, such as faces. If you have a smartphone with facial recognition capabilities, machine vision is a part of your daily life.
Manufacturers, especially high-volume manufacturers, face a challenge: How can they ensure product quality with minimal damage to their profit margins? One option is manual quality control. Manufacturers assign employees to the task of checking product quality by hand. This approach is less than ideal—for employers and employees alike. It entails high labor costs, poor ergonomics, and unforgiving quotas.
The better solution is machine vision for quality control. Some manufacturers already use machine vision for quality control, including household names such as BMW, Canon, and Audi. They integrate machine vision into their manufacturing lines to detect quality issues faster than by hand, including some too subtle for human eyes.
Machine vision does not eliminate people from the quality control process. Instead, it works alongside human quality controllers. BMW’s Dingolfing plant provides a great example of this collaboration; when machine vision detects quality issues in a vehicle, it alerts the human-staffed final inspection team. These employees then judge whether the alert requires action. In other words, machine vision doesn’t replace human judgment but rather focuses quality controllers on high-value tasks, optimizing their work.
Synaptiq is an AI and data science consultancy based in Portland, Oregon. We collaborate with our clients to develop human-centered products and solutions. We uphold a strong commitment to ethics and innovation.
Contact us if you have a problem to solve, a process to refine, or a question to ask.
You can learn more about our story through our past projects, blog, or podcast.