Enhanced Deep Stacked CapsNet Ensemble Gazelle Neural Network for multi-level fabric defect classification

Abstract

Fabric detection in the materials industry plays a vital role in the global economy, making effective quality control measures essential to ensure product value and reduce manufacturing waste. With the rise of Industry 4.0, manufacturing companies have been striving to develop automated fabric defect detection systems to overcome the limitations of traditional manual inspection. However, because of challenges in creating highly effective fabric defect detection methods with strong noise resistance, conventional systems often struggle to capture intricate fabric details and accurately distinguish between defect types. To address these challenges, this research introduces an innovative approach called the Enhanced Deep Stacked capsNet Ensemble Gazelle Neural Network (EDSEGNN) for multi-level local and global defect classification. By incorporating the Window-aware Guided Image Filtering technique, image quality and resolution are enhanced, enabling the detection of fine fabric details. Additionally, the Wavelet Packet Transform aids in segmenting fabric defects by identifying small patterns through varying frequency waves. In the final stage, the EDSEGNN model performs local defect identification and multi-level global defect classification, distinguishing between normal and defective patterns while categorising defect types like discoloration, stains, foreign objects, cuts, holes, thread issues and metal contamination. The proposed method achieves impressive results, with a peak accuracy of 99.8%, along with high recall (99.5%) and F1-score (99%), compared with existing methods. The proposed approach offers a highly accurate and robust solution to the challenges faced by traditional fabric defect detection systems, representing a valuable advance in automated quality control for the materials industry.