AUTOMATED DEFECT DETECTION IN FABRICS USING DEEP LEARNING MODELS

AUTOMATED DEFECT DETECTION IN FABRICS USING DEEP LEARNING MODELS

Dr Tayar Y1, Sohana B2, Usha Sravanthi P3

2,3B Tech Students, Dept of Computer Science and Engineering, Lingayas Institute of Management & Technology, Madalavarigudem, Vijayawada, Andhra Pradesh, India

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Abstract - Fabric defect detection plays a crucial role in ensuring product quality in textile manufacturing. In this project, we propose a robust framework for fabric defect detection leveraging deep learning techniques, particularly Res-Net architecture. Our approach integrates preprocessing techniques for noise reduction and feature enhancement, followed by Res-Net-based convolutional neural networks for accurate defect classification. We conduct extensive experiments on benchmark datasets to validate the effectiveness of our proposed method. Keywords: Fabric Defect Detection, Deep Learning, Res-Net, Convolutional Neural Networks, Textile Manufacturing.

Key Words:  Convolutional Neural Networks, enhancement, Res-Net-based convolutional, leveraging.