Real-Time Defect Detection Cuts Quality Reject Rate by 83% on the Production Line
The Challenge
PrecisionParts manufactures metal components for the automotive sector. Their manual QC process reviewed only 12% of output due to throughput constraints, and a 4.2% defect escape rate was causing costly recalls and customer penalties. They needed 100% inspection coverage without slowing the line.
Our Solution
We designed and deployed an edge-based computer vision pipeline: high-resolution industrial cameras mounted at key inspection points feed frames into a custom YOLOv8 model trained on 14,000+ labelled defect images (scratches, cracks, dimensional mismatches, surface pitting). Defective parts are flagged and auto-diverted by a pneumatic reject gate in under 80ms. A live dashboard tracks defect trends by shift, machine, and defect type.
The Results
100%
Inspection coverage (was 12%)
83%
Reduction in defect escape rate
< 80ms
Detection latency per component
₹42L
Annual recall & rework cost avoided
“We went from inspecting 12% of parts to 100% — and catching defects we didn't even know existed. The payback period was under 4 months.”
Vikram Desai
VP Operations, PrecisionParts
Tech Stack
Project Details
Key Outcomes
- 100% — Inspection coverage (was 12%)
- 83% — Reduction in defect escape rate
- < 80ms — Detection latency per component
- ₹42L — Annual recall & rework cost avoided
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