Combining AI and Creative Design for Automated Fabric Defect Analysis and Pattern Generation from Common Imperfections
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Common perception holds that fabric imperfections are unavoidable and contribute to economic losses and degradation of the environment. This study offers a new perspective by reimagining these shortcomings as opportunities for innovative design. This study takes an approach, using Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN) to not only identify fabric defects but also transform them into the unique visual designs. The dataset of 3,000 fabric defects from local and open datasets has been added unbiasly and equally.
The CNN model, developed using Keras, attains an excellent accuracy of 81.65% over 11 defect types, encompassing structural concerns as well as surface and seam anomalies. The GAN subsequently converts these defects into innovative patterns, while the CNN is employed once more to identify and categorize each defect in the newly created designs. This unique technique connects with SDG 12: Responsible Consumption and Production, fostering sustainable textile practices, minimizing waste, and appreciating beauty in imperfection.
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