Abstract:
Fabric quality assessment is a critical process in the textile sector that has, until now, relied on
manual checking, which is time consuming, untrustworthy, and prone to errors. This causes
colossal economic losses and non-standard production. Manual methods are no longer efficient
enough to detect subtle and advanced defects with increasing demand for high-quality materials.
There is an urgent need to have an automated, scalable, and intelligent system for assessing the
quality of fabrics with accuracy.
To counter this, a machine learning solution was developed that included EfficientNetB3 with a
SE Block Attention Module for texture classification and defect detection. It uses transfer learning,
attention blocks, and texture analysis techniques (GLCM + LBP) to improve accuracy and
generalization. A Progressive Web App (PWA) was developed using HTML, CSS, JavaScript
(Bootstrap), and a FastAPI backend. It also has a PostgreSQL database to maintain prediction
history and provide reports.
The model achieved test accuracy of 85% in defect detection and performed sturdily on different
fabric textures. Including texture classification made the system more dependable and expressive.
Data augmentation, dropout regularization, and model fine-tuning led to performance
improvements. The system is real-time assessable, user-friendly, and responsive. Future
improvements will include dataset enrichment, edge deployment, and real-time analyzing abilities.
