Task: Preprocessing for Convolutional Neural Network

SUMMARY

In textile quality control, preprocessing images is essential for ensuring that your models can accurately detect defects while maintaining the integrity of textile details. How can you enhance your image dataset to make defects stand out without losing crucial information about the fabric?

In this lesson, you will explore key preprocessing techniques, including resizing, normalization, and data augmentation, that will optimize the quality of textile inspection images. You will learn how resizing and normalization improve data consistency, and how augmentation techniques like rotation, flipping, and scaling can increase the variability of your dataset to make your models more robust.

You’ll gain hands-on experience using Python to implement preprocessing workflows, preparing your textile image data for CNNs. By the end of the lesson, you’ll be able to apply preprocessing methods to your own image datasets, significantly improving defect detection and model performance in real-world textile quality control applications.

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.