Computer Vision for Zero-Defect Assembly Lines
Enabling zero-defect manufacturing through edge based visual quality control
The Challenge
Our Approach
We deployed an Edge-based Computer Vision System trained on real production footage. Features included:
- Real-time video feed analysis to flag defective units on the assembly line
- Detection of micro-level inconsistencies using transfer learning from pre-trained models
- Integration with conveyor belt PLCs to auto-reject faulty products
- A no-code dashboard for QA leads to retrain models using new defect images
Latency and lighting variability were solved via hardware-optimized models running on NVIDIA Jetson devices.
Stats
Defect escape rate cut from 8.2% to 1.3%
The Outcome
- Reduced visual defect escape rate from 8.2% to under 1.3%
- Cut manual inspection time by 70%
- Increased first-pass yield by 19% in 60 days
- Built an internal “defect library” for future predictive maintenance use
Case Study
- All Posts
- AI Agents
- AI Chat Bot
- AI Kickstater
- Computer Vision
- Data Engineering
- Gen AI
- Machine Learning
Home Compute Vision Smart Retail Analytics CV-Powered Insights From In-Store Footage Elevating in-store experiences with vision-powered retail intelligence The Challenge...
Home Compute Vision AgriTech – Crop Health Monitoring at Scale Aerial CV That Sees What the Human Eye Can’t Advancing...
Home Data Engineering Multinational Retail Chain Modernizing Retail Intelligence with Scalable Data Infrastructure Unifying retail data pipelines to fuel real-time...