Please use this identifier to cite or link to this item:
Title: Fabric defect detection algorithm based on PHOG and SVM
Authors: Cuifang, Zhao
Yu, Chen
Jiacheng, Ma
Keywords: Defect detection;Fabric image;Pyramid histogram of edge orientation gradients;Support vector machine
Issue Date: Mar-2020
Publisher: NISCAIR-CSIR, India
Abstract: In order to effectively improve the detection probability for different types of fabrics and defects, a fabric defect detection method based on pyramid histogram of edge orientation gradients (PHOG) and support vector machine (SVM) has been proposed. The algorithm combines fabric texture statistical method and machine learning method. It has two main parts, namely the feature extraction and classification. The detection process mainly includes image segmentation, PHOG feature extraction, SVM model training and detection classification. The simulation results show that, based on the detection rate and the false alarm rate, the algorithm has a good detection and classification effect, has a certain robustness, and can be applied to the actual production department.
Page(s): 123-126
ISSN: 0975-1025 (Online); 0971-0426 (Print)
Appears in Collections:IJFTR Vol.45(1) [March 2020]

Files in This Item:
File Description SizeFormat 
IJFTR 45(1) 123-126.pdf670.17 kBAdobe PDFView/Open

Items in NOPR are protected by copyright, with all rights reserved, unless otherwise indicated.