Treffer: YMaskNet: A Deep Learning Based Handloom Fabric Defect Detection Technique.

Quality control is crucial for handloom fabric manufacturers, as undetected defects can cause financial losses and harm their reputation. Traditional inspection methods, achieving only 60–75% accuracy, are inadequate for maintaining high-quality standards. To overcome this limitation, we propose YMaskNet, an automated defect detection system specifically designed for handloom fabrics, operable with or without human supervision. YMaskNet integrates YOLOv4 with a MobileNetV2 backbone, Bidirectional Feature Pyramid Network (BiFPN), and Mask R-CNN to deliver efficient and precise defect identification. MobileNetV2 enables lightweight and effective feature extraction, while BiFPN enhances multi-scale feature fusion, improving the detection of defects of varying sizes. Mask R-CNN adds further precision by segmenting defect regions accurately. Our proprietary YMask dataset supports training and evaluation of the model. Experimental results demonstrate that YMaskNet significantly outperforms traditional methods in both precision and recall while maintaining real-time performance. The system effectively detects small and complex fabric defects, ensuring higher reliability and operational efficiency. Overall, YMaskNet provides a robust, scalable, and automated solution for maintaining product quality in textile manufacturing and strengthening the competitiveness of the handloom industry through intelligent, data-driven quality control. [ABSTRACT FROM AUTHOR]