AI Visual Quality Inspection

Overview

A food packaging operation running 3 production lines at 200 units per minute relied on human inspectors who could only check a sample of output. Defective products — incorrect labels, damaged seals, foreign objects, fill-level errors — were reaching customers and triggering costly recalls and reputation damage. We deployed a computer vision quality inspection system that examines every single unit at full line speed.

The Challenge

Production lines move fast and defects are subtle — a seal that's 95% complete looks fine to the eye. The system needed to handle variable lighting conditions, different product SKUs with different visual profiles, and the vibration and speed of a real production environment. False reject rates needed to be low enough to not waste product, while catch rates needed to be nearly perfect.

Our Approach

We installed high-speed cameras at inspection points on each line with controlled lighting. Custom-trained models detect defect types specific to the products: label alignment, print quality, seal integrity, fill level, and foreign object detection. Each model was trained on thousands of images of good and defective products captured from the actual production environment. Defective products are automatically rejected by pneumatic diverters. All inspection data feeds into a quality dashboard for trend analysis and root cause investigation.

Key Features

• Full-speed inspection of every unit on line
• Multi-defect type detection in single pass
• SKU-specific inspection profiles
• Automatic rejection of defective products
• Quality trend dashboard with root cause analysis
• Shift-level performance reporting
• Integration with MES and ERP systems

Results

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