Abstract
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.
Highlights
Image processing operations such as preprocessing, feature selection, classification and pattern detection enable the imaging application to analyze the product images at its granular level
Many feature extraction techniques are derived from linear methods like principal component analysis (PCA)
Weld defect identification approach consumes 32.35% lesser memory space than fabric defect classification using neural network (NN) and 7.81% lesser than fabric defect detection scheme, that are used to improve the visual appearance of an image or used to convert the image to a form, which can be better suited for further analysis in the subsequent stages by a human or a machine[6]
Summary
Image processing operations such as preprocessing, feature selection, classification and pattern detection enable the imaging application to analyze the product images at its granular (pixel) level. The homogeneous pixels with predominant features are grouped to form various image object segments. With the growth of computer and digital image processing technology, computer vision is used to identify the fabric defects to replace the traditional methods. Image classifier techniques are applied to classify the segmented image portion and generate multiple image object classes (i.e., normal, defect, minimal defect) based on the intrinsic property of the pixel similar to a particular class. To identify the normal or defect nature of the image, pattern detection methods are used to train the normal and defective image patterns from standard product images. The test images are verified by checking the pattern similarity with trained image patterns to accurately identify whether the product images are defective or normal patterns
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call