Fabric surface defect classification and systematic analysis using a cuckoo search optimized deep residual network

Fabric defects can significantly impact the quality of a textile product. By analyzing the types and frequencies of defects, manufacturers can identify process inefficiencies, equipment malfunctions, or operator errors. Although deep learning networks are accurate in classification applications, som...

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Bibliographic Details
Published in:Engineering science and technology, an international journal an international journal, 2024-05, Vol.53, p.101681, Article 101681
Main Authors: Mewada, Hiren, Pires, Ivan Miguel, Engineer, Pinalkumar, Patel, Amit V.
Format: Article
Language:English
Subjects:
Decision-making
Deep Learning
Fabric defect
Industrial Growth
Optimization
Sustainability labeling
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Summary:Fabric defects can significantly impact the quality of a textile product. By analyzing the types and frequencies of defects, manufacturers can identify process inefficiencies, equipment malfunctions, or operator errors. Although deep learning networks are accurate in classification applications, some defects may be subtle and difficult to detect, while others may have complex patterns or occlusions. CNNs may struggle to capture a wide range of defect variations and generalize well to unseen defects. Discriminating between genuine defects and benign variations requires sophisticated feature extraction and modeling techniques. This paper proposes a residual network-based CNN model to enhance the classification of fabric defects. A pretrained residual network, ResNet50, is fine-tuned to classify fabric defects into four categories: holes, objects, oil spots, and thread errors on the fabric surface. The fine-tuned network is further optimized via cuckoo search optimization using classification error as a fitness function. The network is systematically analyzed at different layers, and the investigation of classification results are reported using a confusion matrix and classification accuracy for each class. The experimental results confirm that the proposed model achieved superior performance with 95.36% accuracy and a 95.35% F1 score for multiclass classification. In addition, the proposed model achieved higher accuracy with similar or fewer trainable parameters than traditional deep CNN networks.
ISSN:2215-0986
2215-0986
DOI:10.1016/j.jestch.2024.101681