Computer vision quality control
Automatically detect manufacturing defects on production lines using cameras and deep learning.
95%
defect detection rate
The problem
No reliable steering on computer vision quality control.
The current process is manual or inconsistent.
Decisions come too late due to weak signals.
Costs and lead times drift without control.
Prerequisites: required data & tools
Required data
- Production images/videos
- labels de défauts
Compatible tools
- AWS Lookout for Vision
- Landing AI
- custom CNN
Not sure you have the data? Our Maturity Auditor can assess your situation in two weeks.
Explore the Maturity Auditor →What we implement in 6-12 months
In 6-12 months: Automatically detect manufacturing defects on production lines using cameras and deep learning. with measured impact on defect detection rate.
Weeks 1-2
Diagnosis
Weeks 3-6
Build
Week 7+
Delivery
Concrete deliverables
Business framing and decision rules for computer vision quality control
Operational engine for computer vision quality control
Steering dashboard with alerts
Action playbook and governance
Expert insight
Detection rate >95% vs ~80% human. Requires camera investment and a trained model.
— Datasive, expertise terrain
Tech maturity
High
Mature solutions, fast deployment
Medium
Maturing tech, requires customization
Emerging
Cutting-edge innovation, R&D approach
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Maturity Auditor
Scorecard + roadmap 90 jours pour cadrer la transformation data/AI.
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