• Defect Reduction

​​

One of the most common use cases of computer vision in manufacturing is quality assurance and defect reduction. If you want to make sure your parts are achieving desired quality benchmarks, automating first pass defect detection is a way to do so while minimizing manual labor. Using tools like procedural defect generation and domain randomization, we can generate a tremendous variety of synthetic data that will help your model perform on even the rarest edge cases.

​

• Autonomous Manufacturing

 

Increased automation is improving worker safety and making lights-out manufacturing a reality. But successful automation depends on flexibility, including the ability to monitor and update the systems running your operation. While performance data you capture can always be used to adjust your machine learning models, synthetic data is the only way to take control of every step of that machine learning pipeline.

​

• Data Scarcity

 

If you’re opening up a new facility or manufacturing a new component, you may be facing a cold start problem where you have little to no training data for the machine learning model you’d like to implement. Synthetic training data that is tailored to your use case (or edge case) can be rapidly generated and used to supplement available data or even trained on exclusively.

​

• Privacy Compliant

 

If you’re monitoring human activity—employee zone detection, security, or otherwise—your models will need to be trained on data that includes humans. Rather than training your model on sourced images of real humans, which in addition to the cost can expose you to data privacy liabilities, consider using synthetic training data. It’s both GDPR and CCPA compliant by default.

 

 

Save yourself time and money by taking complete control of your machine learning workflow with synthetic data.