Fabric Quality Assurance System: Defect Detection and Image Reconstruction using Gen AI
Keywords:
autoencoders, Convolutional Neural Network, Generative Artificial Intelligence, Kernel Density Estimation.Abstract
Textile waste comprises of one of the largest categories of waste produced, both in India and worldwide. Determining the quality of fabrics and discarding the damaged ones at an initial stage during mass production is essential to maintain a standard of optimal quality. Additionally, it is vital to consider the efficiency of existing systems a parameter to determine the performance of defect detection process. This paper aims to address the importance of robust textile defect detection by proposing one of the best performing anomaly detection algorithms to implement binary classification and a customized Convolutional Neural Network (CNN) to implement multiclass classification. In addition to this, the concept of Generative Artificial Intelligence (GenAI) was also incorporated where new images were reconstructed using autoencoders and then predicted based on the concept of Kernel Density Estimation (KDE). Binary and multiclass classification were performed on two datasets, where one was obtained from Kaggle and another was custom made. Image reconstruction was performed only on the dataset obtained from Kaggle. The performance of these algorithms implemented and proposed was analyzed based on various evaluation metrics.
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