Optimized Attention Augmented Residual Convolutional Neural Network with Fa-Resnet for Fabric Defect Detection

• Sujitha Ravichandran

  Assistant Professor, Department of Computer Science and Engineering, Annapoorana Engineering College, Periya Seeragapadi, Salem, Tamil Nadu, India.

• Venkatasalam Kandasamy

  Professor, Department Of Computer Science and Engineering, Mahendra Engineering College, Namakkal, Tamil Nadu, India.

Fabric defects impact textile quality and production efficiency. This study proposes an Optimized Attention Augmented Residual Convolutional Neural Network with FA-ResNet for Fabric Defect Detection (AARCNN-FA-ResNet-FDD). Images from a fabric defect dataset are pre-processed using Multiple Local Particle Filter (MLPF), followed by feature extraction using Synchro Transient Extracting Transform (STET). Key texture features are classified using AARCNN-FA-ResNet, with parameters optimized via the Lotus Effect Optimization Algorithm (LEOA). The method significantly improves classification accuracy, precision, and reduces computation time compared to existing models like FSDC-CSO-DRN, LSTM-TC-FDD, and CNN-ATCD-FDD, achieving up to 99% classification accuracy.

DOI: 10.61416/ceai.v27i4.9588

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