Artificial Intelligence based Optimization with Extreme Gradient Boosting for Fabric Defect Detection and Classification Model

Abstract

The process of recognizing abnormalities or defects in fabric is termed Fabric defect detection. Fabric defects may arise because of several factors like wear and tear, manufacturing errors, and damage during transportation. Finding such defects is vital to guarantee the durability and quality of the finished product. Among several image classifier tasks, Deep learning (DL) is considered a successful one and has been implemented in fabric defect detection. Therefore, this study develops an Improved Dragonfly optimization with Deep Learning based Fabric Defect Classification (IDFODL-FDC) technique. The proposed IDFODL-FDC technique categorizes the fabric images as defective or not. To do this, the IDFODLFDC technique employs feature extractor based on MobileNet model. In addition, the hyperparameter tuning of the MobileNet model performed via the IDFO technique. Finally, extreme gradient boosting (XGBoost) method is used for fabric defect classification. A series of experiments have been conducted to reveal the enhanced performance of the IDFODL-FDC method. The simulation values stated that the IDFODL-FDC method for fabric defect detection demonstrated good performance and has the potential to increase the efficiency and accuracy of quality control processes in the textile industry.