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Real-Time Defect Detection Across 12 Manufacturing Sites
99.2% defect detection accuracy
99.2%
Defect detection accuracy
94%
Escape rate reduction
3x
Inspection throughput increase
12
Sites deployed
The Challenge
A precision parts manufacturer relied on human QC inspectors across 12 plants, resulting in inconsistent defect detection rates, high escape rates for subtle defects, and inspection bottlenecks on high-throughput lines.
Our Solution
We built a YOLOv8-based computer vision system trained on 80,000+ annotated images of defective and non-defective parts. The system processes 4K camera feeds in real time at sub-100ms latency, running on NVIDIA edge GPUs at each site with a central dashboard aggregating site-wide quality metrics.
The Outcome
Defect detection accuracy reached 99.2% across all product lines. Escape rate dropped by 94%, and inspection throughput increased by 3x. The central dashboard gave QC leadership full visibility across all 12 sites for the first time.
Tech Stack
PythonYOLOv8PyTorchOpenCVNVIDIA JetsonFastAPIReactInfluxDBGrafana
Category
Computer VisionBuild something similar?
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