Case · 2026-04-11

Reflective Metal Housing Grading: Trained on Good Samples Only, 94% Fewer Escapes

Stable grading under the double challenge of reflection and sample scarcity

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Reflective metal surfaces are a recognized vision challenge: the same scratch appears and vanishes with angle. Add scarce defect samples and conventional methods stall. DaoAI's good-sample-only route sidesteps the sample problem, handing uncertainty to the model.

良品仅良品训练
5min0 代码换型
−94%缺陷逃逸

A consumer-electronics plant produces metal mid-frames and housings finished by anodizing, brushing or polishing, giving strongly reflective surfaces. Appearance defects include scratches, dents, pitting, color variation, oxidation spots and knocks, requiring grade sorting (A ships, B downgrades, defects rejected). Strong reflection makes defects appear and disappear across lighting angles, leaving imaging highly unstable.

The difficulty compounds on samples: high-value defects such as fine scratches occur rarely and are hard to collect in volume for training; meanwhile models are many and turn over fast, so each new housing needs redeployment. The plant had to solve reflective imaging while keeping stable grading under scarce defect samples and frequent changeover.

DaoAI solution: go live trained on good samples only, with zero-code changeover for micron-level grading

DaoAI adopts a good-sample-only strategy: build a normal-appearance baseline from qualified housings, and flag any deviation as a defect—no need to pre-collect scarce defect samples. Paired with a multi-angle optical setup that suppresses reflection, the system identifies and grades scratches, pitting and color variation at micron-level precision.

  • Goes live trained on good samples only, bypassing the scarce-defect-sample bottleneck of reflective metal
  • Micron-level precision on scratches, dents, pitting, color variation and oxidation spots
  • Zero-code changeover lets a new housing switch grading baselines on site
  • Auto-grades into A/B/reject, cutting defect escapes 94% versus manual inspection

Reflection wears out human eyes chasing the light; the model cares about one thing—does it look like a good sample.

After go-live, reflective housings are graded automatically and stably, with A/B/reject calls no longer hostage to inspector fatigue or viewing angle. The most critical fine scratches and pitting are reliably captured, defect escapes fell 94% versus the prior manual process, downgrades and customer complaints dropped together, and new models go live with zero-code changeover.

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