Fabric Defect Detection: A Novel Approach Using Hybrid Deep Learning and Image Segmentation
Abstract
Abstract: Fabric defects significantly impact the textile industry, leading to production delays, material waste, and customer dissatisfaction. Existing image processing methods for fabric defect detection often struggle with complex defect patterns or require extensive training data for specific defect types. This paper proposes a novel approach that combines deep learning and image segmentation techniques for robust and adaptable fabric defect detection. This hybrid approach leverages the power of deep learning for feature extraction and image segmentation for precise defect localization.
References
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