Case Study

VisionAI – quality control with computer vision.

We developed and implemented a computer vision platform powered by neural networks that automatically detects manufacturing defects on a conveyor belt in real time. The solution reduced the inspection time for each part from 5 seconds to 0.3 seconds and decreased the defect rate by 40%.

Context and Goal

Success Criteria

Business metrics and operational KPI.
Data readiness and integration quality.
Security and compliance requirements.

Tasks

What needed to be solved and why it mattered for the business.

Data Collection and Annotation

We compiled 50,000 images of parts and manually labeled all types of defects to train the model.

Augmentation and balancing

We applied data augmentation (varying angles, noise, lighting) and rebalanced the classes to improve model robustness.

Solution Milestones

How we built delivery: from hypothesis to production.

Model training.

YOLOv8 was used for defect detection; the model achieved 97% mAP on validation.

Pipeline integration

A REST API and an embedded controller module have been developed; the model is updated without halting the production line.

Results

Business impact validated by measurable outcomes.

Decreasing marriage rates

We achieved a 40% reduction in defects within just 3 months of launch.

Technology

Tools and engineering stack used in delivery.

YOLOv8 + PyTorch

The model was built using PyTorch, and YOLOv8 was employed for real-time defect detection.

FAQ

Answers to common questions about this case.

How quickly can a system be implemented?

The pilot project lasts 4-6 weeks, after which a phased rollout to all lines is possible.

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