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Automate surface inspection, cosmetic quality checks, and reject decisions at line speed.
This application is designed for teams searching for computer vision defect detection systems that can catch scratches, dents, cracks, missing material, contamination, print issues, and finish variation before defects reach the customer.
Built For Production
Tuned for real line conditions, traceability requirements, and fast operator decisions.
Search Intent
Computer Vision Defect Detection
System Compatibility
Designed for production lines where vision has to fit into existing cameras, PLCs, controllers, and factory automation standards without forcing a complete hardware reset.
System Compatibility
Built to work with common machine vision camera ecosystems used across inspection and automation deployments.
Basler
Allied Vision
Baumer
Hikvision99%+
Defect detection accuracy on trained defect classes
24/7
Inline inspection without operator fatigue
ms
Real-time inference for fast production lines
<2 weeks
Typical pilot deployment timeline
Production Challenge
Manual visual inspection becomes inconsistent when production scales, part finishes change, or defects appear only under specific lighting angles. Manufacturers usually search for defect detection AI when escape rates, rework, and customer complaints start increasing faster than teams can control.
Solution Approach
Orama combines camera setup, image capture strategy, defect annotation, model training, threshold tuning, and edge deployment into one production workflow. The result is a system that separates good parts from non-conforming parts with traceable evidence and stable performance on the shop floor.
Typical Project Flow
Capabilities
Deployment Workflow
Use Cases
Business Outcomes
Frequently Asked Questions
It can be trained for surface defects, assembly defects, contamination, print issues, dimensional anomalies visible in images, and many other visual non-conformities as long as the imaging setup captures them reliably.
Yes, but success depends heavily on optics and lighting. We usually solve this with the right illumination angle, enclosure design, and sometimes multi-view image capture before model training starts.
A pilot often starts with a focused dataset of good parts and the main reject categories. The exact number depends on defect variability, but the goal is to cover real production variation rather than collect a massive dataset too early.
Yes. The inspection output can be integrated with PLCs, reject mechanisms, stack lights, HMIs, or MES systems so the AI result becomes part of the production control loop.
Next Step
We can adapt the same platform for your product geometry, lighting, line speed, traceability rules, and reporting needs without forcing your team into a generic workflow.
Project Fit