Autonomous Visual Quality Control System
An AI-based visual inspection solution that minimizes the need for labeled data, self-calibrates from reference master data and generalizes defect logic across product variants.
Multi-light · macro/micro · 3D metrology
Heat map, OK / NOK / Conditional decision label and defect location — on one decision screen.
Quality control for microscopic defects depends heavily on operator attention; bent pins, shell cracks, epoxy deviations and surface scratches often sit at the limit of the human eye. Classic AI solutions must be re-labeled and re-trained for every defect and every product variant.
With a frozen vision network, an anomaly memory bank and reference master data, only the visual signature of the “good part” is learned. Deviating regions are flagged on a heat map and the decision engine classifies OK / NOK / Conditional. Defect logic learned on one connector type generalizes to variants with different geometry.
Off-the-shelf AI camera engines offer quick setup for specific products and defect types but are limited to in-camera models. Here, AI is not a single feature embedded in a camera: the camera, lighting, lens, imaging scenario, 3D metrology, reference data management, anomaly mapping and decision algorithm are designed together, end to end.