Why Image Acquisition Makes or Breaks Machine Vision Inspection | Qualitas

Описание: Most inspection systems fail before the AI even runs. The image was never consistent to begin with.
In this video, Raghava Kashyapa (CEO, Qualitas Technologies) breaks down why image acquisition is the hardest engineering problem in automated inspection.
Using a tweezer tip inspection system as a live example, he walks through exactly how Qualitas controls every variable that affects the image: pressure, contact point, orientation, lighting, and optics.
The image acquisition challenges in this system:
→ Reflective metal surfaces that hide edges and create false gaps
→ Pressure variation that changes how tips close — and what the camera sees
→ Contact point shifts that alter gap and alignment in the image
→ Orientation drift that makes openings disappear under backlight
→ Lighting geometry that must reveal edges, not wash them out
How we solved it:
→ Load cell-controlled plunger — same force, every cycle
→ Standardized contact point — locked, not assumed
→ 5 images per part across defined orientations and pressure conditions
→ Backlight + controlled optics designed around reflective metal
→ Identical image output across multiple machines
The result:
Once the image is stable, inspection becomes measurable. Pass/fail decisions are consistent — across shifts, operators, and production lines.
🔬 Qualitas Technologies — Machine Vision Inspection Systems