Computer Vision Quality Inspection Prototype
95% defect detection accuracy — a validated proof of concept for production
Achieved 95% detection accuracy during testing
Reduced inspection time by 78%
Delivered a validated proof of concept ready for production planning
Overview
Automated visual quality inspection can reduce defect escape rates and inspection labour costs significantly — but manufacturing teams need confidence in detection accuracy before committing to production systems. We designed and tested a computer vision prototype that demonstrated feasibility with real production samples, giving the client a validated architecture and performance benchmark to support their investment decision.
The Challenge
A manufacturing team needed to detect product defects automatically but wanted to validate technical feasibility and detection accuracy before investing in production systems.
The Solution
Designed and tested a computer vision prototype capable of identifying visual defects using image recognition and object detection models, with performance benchmarking against manual inspection.
How We Approached It
Dataset Collection
Worked with the production team to collect and label a representative image dataset covering all known defect types and lighting conditions.
Model Selection
Evaluated and compared YOLOv8 for real-time detection against CNN classifiers, selecting the optimal architecture per defect type.
Training & Validation
Trained on 80% of the labelled dataset, validated on 20%, and measured precision, recall, and false positive rates against production requirements.
Performance Report
Delivered a full technical report with accuracy metrics, hardware requirements, and a recommended production architecture.
Key Features Built
Results & Impact
Achieved 95% detection accuracy during testing
Reduced inspection time by 78%
Delivered a validated proof of concept ready for production planning
Technologies
Ready to get started?
Tell me what you're building and I'll give you my honest assessment of the best approach.