Task: Convolutional Neural Network for Quality Control

SUMMARY

Defect detection in textile manufacturing is a challenging but crucial task for ensuring product quality. With Convolutional Neural Networks (CNNs), you can automate the process of identifying fabric and garment defects from images. But how do you build and train a CNN that can effectively detect defects, even in complex and subtle patterns?

In this hands-on lesson, you’ll learn how to create a CNN model designed to detect fabric and garment defects in images. You’ll explore the key parameters of the model such as learning rate, batch size, and number of epochs, and learn how to optimize them for better performance in textile quality control.

Once your CNN model is built, you’ll evaluate its effectiveness using relevant performance metrics, such as accuracy, precision, recall, and F1 score, to ensure the model is reliable for detecting defects in real-world scenarios. After completing the training, you will be able to confidently assess how well your model performs in identifying defects in textile products.

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 Internet of Things (IoT) and Machine Learning (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.