Image based defect detection becomes a demanding task in estimating the quality of intermediate and end products in  fabric and granite manufacturing, pipeline installation in heavy industries. A fabric defect detection scheme improves the quality for image defect detection and achieves higher accuracy to detect images. But, the image detection is complex in noisy applications. When the image size is large, it provides the false positive detection. The automated fabric defect classification techniques were used to analyze the ability of classifiers that employed in defect inspection systems with geometrical features. But in defect classification technique, level of accuracy is not satisfactory and real-time constraints needs to be addressed. Fabric defect detection is a significant problem in fabric quality control processing, and need to develop fast, efficient, reliable and real-time defect detection through image analysis techniques. Our research work on filtering, pattern classification and pattern detection aims to identify normal and defective image patterns from trained class patterns of the training image dataset.


Automated fabric defect classification; filtering; Pattern classification; Feature Extraction; Segmentation; Principal Component Analysis(PCA); Peak Signal-to-Noise Ratio (PSNR); Artificial Neural Network (ANN); Particle Swarm Optimization(PSO)

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DOI: https://doi.org/10.26483/ijarcs.v11i2.6514


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