Designing a Vision-Based Defect Detection System for the Production Line

Defect detection systems that succeed on a real production line share a few habits: clean imaging, careful labeling, robust evaluation, and respectful integration with the existing line and team. This course walks through those choices in the order they typically arise during a project. You will work through written design exercises that mirror how a small automation or data team would plan a defect detection system. The emphasis is on the practical tradeoffs that matter when line speed, false positives, and operator trust are all under pressure. What you'll learn: - Plan imaging setups including lighting, camera resolution, and trigger synchronization - Build labeling protocols that produce clean training data with clear defect definitions - Compare modeling approaches including classification, detection, segmentation, and anomaly detection - Evaluate models with operationally meaningful metrics such as false positive rate and missed defects - Plan deployment with attention to inference latency, line speed, and integration with PLC systems - Design retraining workflows that handle new defect types and changing production mixes The course progresses from imaging through labeling, modeling, evaluation, and finally line integration. A capstone written exercise asks you to draft a one-page design for a defect detection system for a specific product and production environment. This course is designed for beginners with some software or engineering background, including data scientists entering manufacturing, automation engineers exploring AI, and students of industrial engineering. No deep manufacturing experience is required. The course treats the system as a design problem you can reason about on paper before any hardware is purchased.