Visual Data Annotation for Quality Control (Pilot)

Type of course:

Digital learning, Lesson

Language:

Duration:

10 minutes

Workload:

2 hours

Proficiency:

Intermediate

Target:

Manager, Professionals, Workers

SUMMARY

What’s the secret to building an AI that can detect objects, recognize faces, or even drive a car? The answer lies in the quality of the visual data you use to train it. But how do you ensure that the data is labeled correctly and ready for machine learning models? That’s exactly what this course is all about.

This course dives deep into the world of visual data annotation, equipping you with the skills to prepare high-quality datasets for machine learning, particularly for anomaly detection.

“Data is only as good as the labels you put on it.” This course shows you how to turn raw visual data into powerful, structured information that drives effective machine learning models. Whether you’re a beginner or looking to refine your annotation techniques, you’ll leave with practical knowledge that’s key to mastering AI applications in the field of quality control, and more.

About The Author

Dilek Dustegor is a Professor of Computing Science at the University of Groningen in the Netherlands. She is interested in bridging the gaps between research, development and implementation using AI and automation. She is pursuing research about modeling, design and analysis of large scale / networked systems using IoT and ML techniques, with a special interest in smart city applications. She is a seasoned educator, and loves using the newest educational technologies for an enhanced learning experience.

Learning outcomes

By the end of this lesson, students will be able to apply specialized annotation techniques (e.g., bounding boxes, pixel-level segmentation) to accurately label visual data for anomaly detection in quality control, focusing on identifying defects, irregularities, or deviations in products and processes.
By the end of this lesson, students will be able to evaluate the impact of annotation quality on anomaly detection models, understanding how precise and consistent labeling of defects directly influences the performance and reliability of AI models used in automated quality control systems.
By the end of this lesson, students will be able to select appropriate annotation tools and platforms tailored for anomaly detection applications in quality control, ensuring that visual data is accurately prepared to detect small defects, outliers, or deviations in real-world manufacturing and inspection environments.