Chat with us!We built a computer-vision system that inspects products on the line in real time, detecting defects and classifying items with accuracy and consistency th...
It catches things a tired human eye misses — every unit, every shift, without fail.
Quality Assurance Manager
We built a computer-vision system that inspects products on the line in real time, detecting defects and classifying items with accuracy and consistency that manual inspection could not match.
| Project Detail | Information |
|---|---|
| Project Type | Web |
| Industry | Manufacturing |
| Technologies | PyTorch, YOLO / CNNs, ONNX / TensorRT, Python, React, Edge devices |
| Delivery Partner | mTouch Labs |
| Primary Outcome | Inspection became fast, consistent, and data-rich, catching defects manual review missed. |
Manual visual inspection was slow, fatiguing, and inconsistent. Defects slipped through to customers, and there was no data on where quality issues originated.
We trained custom detection and classification models on labeled defect imagery and deployed them at the edge for real-time, low-latency inspection on the line.
The system flags defects instantly, classifies type and severity, and feeds a dashboard that pinpoints where and why quality issues arise.
Detection and classification model training
Real-time object and defect detection
Optimized edge inference
Vision pipeline and tooling
Inspection analytics dashboard
On-line, low-latency inference
Collected and annotated defect imagery across conditions.
Trained and validated detection and classification models.
Quantized and optimized models for real-time inference.
Connected to cameras and reject mechanisms.
Looped flagged edge cases back into training.
Inspection became fast, consistent, and data-rich, catching defects manual review missed.
Computer-vision inspection delivered consistent, real-time quality control and turned visual data into insight that drives upstream improvements.
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View case studyWhat businesses ask us most often about this project and how we built it.
It can detect and classify objects, defects, and anomalies in images or video — for example surface flaws, missing components, or mislabeled items on a production line.
Yes. Models are optimized and deployed at the edge for low-latency, real-time inspection directly on the line.
On trained defect classes the system exceeds 98% detection accuracy and stays consistent across shifts.
Yes. An active-learning loop feeds flagged edge cases back into training to keep improving on rare defects.
Yes. It connects to cameras and reject mechanisms and feeds analytics into your quality systems.
mTouch Labs combines AI-powered development with deep industry expertise to deliver solutions faster.
