Computer vision has been learning how to spot manufacturing flaws. The pandemic is accelerating that education.
What’s happening: Companies like Instrumental and Elementary are making AI-powered cameras that automate the spotting of damaged or badly assembled products on factory assembly lines, Wired reports. (For the record, deeplearning.ai’s sister company Landing AI is, too).
How it works: Instrumental’s quality-control system first learns to recognize components in their ideal state and then to identify defects. It can spot faulty screws, disfigured circuit boards, and flaws in the protective coating on smartphone screens.
- Cameras along the assembly line take photos of products in the making. The manufacturer’s engineers review the images and label defects. The labeled data is used to fine-tune the system.
- Manufacturers often don’t allow outsiders direct access to their equipment, so Instrumental’s engineers typically tweak systems on-site. Amid the pandemic, though, five clients are allowing the company to monitor the assembly line remotely, making it possible to update the computer vision model on the fly.
Coming soon: Elementary plans to install robotic cameras in a U.S. Toyota plant. Workers will place a completed part beneath the camera for inspection, then press a button to indicate whether they agree with the robot’s assessment to fine-tune the model.
Behind the news: Omron, Cognex, and USS Vision have sold non-neural inspection systems for decades. Neural networks are making their way into the field as engineers develop techniques for learning what flaws look like from small numbers of examples.
Why it matters: Earlier automated inspection systems use hand-coded rules to identify specific flaws. Machine learning promises to be more adaptable and quicker to deploy. That could speed up assembly lines and cut manufacturing costs.
We’re thinking: The ability to learn from small amounts of data is the key to many applications of deep learning that are still beyond reach. We look forward to continued progress in this area.