Abstract
In the textile manufacturing industry, fabric defect detection becomes a necessary and essential step in quality control. The investment in this field is more than economical when reduction in labor cost and associated benefits are considered. Moreover, the development of a wholly automated inspection system requires efficient and robust algorithms. To overcome this problem, in this paper, we present a new fabric defect detection scheme which uses the local homogeneity and neural network. Its first step consists in computing a new homogeneity image denoted as H-image. The second step is devoted to the application of the discrete cosine transform (DCT) to the H-image and the extraction of different representative energy features of each DCT block. These energy features are used by the back-propagation neural network to judge the existence of fabric defect. Simulations on different fabric images and different defect aspects show that the proposed method achieves an average accuracy of 97.35%.
Highlights
Defect detection is one of the main steps in quality control of manufacturing processes
In order to characterize the frequency domain within texture image, this paper proposes to use the features energy distribution in the 2D-discrete cosine transform (DCT) domain from the H-image, which is presented as follows [27]: (i) The horizontal energy value of the DCT block: EH
The dataset used in this work is provided by PARTNER textile industry in Tunisia [30]
Summary
Defect detection is one of the main steps in quality control of manufacturing processes. Fabric defects are responsible for nearly 85% of the defects found in the garment industry [1]. It has been observed [2] that price of textile fabric is reduced by 45% to 65% due to defects. It is imperative, to detect, identify, and prevent these defects from reoccurring. As confirmed by [3, 4], there are more than 70 kinds of fabric defects defined by the textile industry. A human fabric inspection achieves a success rate about 60 to 75% [3].
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