AI Development

Computer Vision QC System

ManuTech Albania

PyTorch OpenCV Edge Computing
99.2% defect accuracy

The Challenge

ManuTech Albania's quality control process relied entirely on manual visual inspection — slow, expensive, and subject to fatigue-related misses. Defect escape rate was 1.8%, leading to costly returns and reputational damage with key clients.

99.2%
Defect Detection Accuracy
1.8%→0.1%
Defect Escape Rate
<30ms
Inference per Frame

The Solution

We deployed a real-time computer vision system on the production line using custom-trained YOLOv8 models running on NVIDIA Jetson edge devices. The system inspects every product at line speed, classifying 23 defect types with 99.2% accuracy.

We collected and annotated a custom dataset of 50,000+ images across lighting conditions and product variants, then trained and validated the model against ManuTech's own quality standards. Inference runs locally on edge hardware, ensuring zero dependency on internet connectivity.

A web-based quality dashboard shows real-time defect rates, trend analysis, and shift-level reports, replacing manual inspection logs entirely.

PyTorch YOLOv8 OpenCV NVIDIA Jetson Python FastAPI

Key Results

99.2%
Defect Detection Accuracy
1.8%→0.1%
Defect Escape Rate
<30ms
Inference per Frame
Project Screenshot 1
Project Screenshot 2
Project Screenshot 3

"We eliminated our defect escapes and freed 6 inspectors to do more valuable work. The payback period was under 4 months."

AH
Altin Hoxha
Operations Director, ManuTech Albania

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