When Visual AI Inspection Feels Impossible: Turn to V-CORTX
A major U.S. onion ring producer was told their raw produce inspection challenge was too complex to automate over 10 defect types, high line speeds, and varying rework decisions made traditional vision impractical. With V-CORTX, Oxipital AI proved otherwise, deploying a multi-class Visual AI solution that is already transforming one of their most difficult inspections
If you think your application is too difficult for Visual AI inspection, think again.
A major U.S. Prepared Foods producer was told by multiple other AI vision providers that their inspection challenge wasn’t possible to automate. The application involved raw produce, moving at high speed, spread unevenly across a conveyor, with more than 10 unique defect classifications, each requiring a different output decision.
The difference between the defects was subtle, but financially significant.
Their goals were clear:
- Increase yield
- Reduce manufacturing costs
- Lower labor dependency
Manual inspection was the only alternative, and it required too many operators to be sustainable.
The Challenge
Complex Defects, Complex Decisions
This was the manufacturer’s most difficult inspection application.
Key hurdles included:
- 10+ defect classifications with varying reject/rework logic
- Fast-moving product spread randomly across the belt
- Visually subtle defects that were difficult even for trained operators
- High accuracy required to avoid unnecessary scrap
Sending good products to trash reduced their yield. Sending bad products forward reduced their quality. The margin for error was small.
Traditional machine vision systems struggled with rule definition. The variability of raw produce made edge-based and color-based approaches unreliable. Other AI providers struggled with data capture and most providers declined the project entirely.
OUR SOLUTION
V-CORTX A all Inclusive Solution
When the manufacturer came to Oxipital AI, we said yes.
Using V-CORTX, our Visual AI inspection platform, we developed and deployed a multi-class inspection model powered by advanced learning techniques. The system was designed to:
- Detect and classify 10+ defect types through AI Models
- Differentiate between rework and scrap decisions
- Operate at production line speed
- Adapt to natural variability in raw produce and production environments
The deployment includes two of our 2D and 3D cameras and has been deployed in production for site acceptance for the last four months.
The IMPACT
Yield Improvement and Cost Reduction
Deployment results are already showing:
- Improved yield by preventing unnecessary scrap
- Reduced manufacturing costs and Lower labor requirements
- Greater consistency than manual inspection
Instead of adding operators to visually inspect complex product streams, the manufacturer is using V-CORTX to make accurate, repeatable decisions at scale.
Beyond “possible”: enabling the applications others avoid
Applications like this are often labeled “too complex” for automation. Too many defect types. Too much variability. Too fast.
That’s exactly where V-CORTX excels.
Oxipital AI does not shy away from challenging inspection problems. We turn around models quickly, support full deployment and testing, and work alongside manufacturers to prove value in real production environments. This deployment goes beyond inspection. The system identifies 10+ defect classifications and seamlessly integrates with multiple robots that automatically bucketize products for either rework or scrap. Coordinating high-speed, multi-class defect detection with robotic picking represents a significant technical challenge, one that Oxipital AI and V-CORTX were uniquely positioned to solve.