Ensemble Deep Learning for Real-Time Textile Fabric Defect Detection and Classification Using YOLOv8 and CNNs

Abstract

Fabric defects are a major challenge in the textile industry, where quality is essential. These defects can appear at different stages of production. By automating defect detection, we can reduce the need for manual checks, speed up the process, and improve productivity. Existing fabric defect detection methods often rely on manual visual inspection, which is time-consuming, subjective, and prone to human error. Complex algorithms may not be suitable for real-time deployment in resource-constrained environments. This research addresses the limitations of existing methods by proposing a deep learning and Ensemble-based approach for fabric defect detection and classification. A custom fabric defect dataset was constructed specifically designed for this research. This paper's proposed approach achieved promising results. YOLOv8m and YOLOv8n-obb variants were fine-tuned for defect detection, achieving a mAP@50 of 0.89. The ensemble approach based on Weighted voting achieved an overall classification accuracy of 90% and a balanced F1 score across six defect classes: Broken-button (95%), Button-hike (93%), Color-defect (86%), Foreign-yarn (90%), Hole (84%), and Sewing-error (88%). This demonstrates the effectiveness of lightweight object detection and the ensemble approach using five CNN models (VGG16, ResNet50, MobileNet, InceptionV3, Xception) for robust fabric defect detection and classification, highlighting its potential for automated quality control in the textile industry.