Fabric defect detection using local homogeneity and morphological image processing

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%.

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Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review

Mathematical Problems in Engineering ◽

10.1155/2020/8189403 ◽

2020 ◽

Vol 2020 ◽

pp. 1-24

Author(s):

Aqsa Rasheed ◽

Bushra Zafar ◽

Amina Rasheed ◽

Nouman Ali ◽

Muhammad Sajid ◽

...

Keyword(s):

Image Processing ◽

Computer Vision ◽

Production Process ◽

Digital Image Processing ◽

Defect Detection ◽

Digital Image ◽

Textile Industry ◽

Future Research ◽

Fabric Defect Detection ◽

Fabric Defect

There are different applications of computer vision and digital image processing in various applied domains and automated production process. In textile industry, fabric defect detection is considered as a challenging task as the quality and the price of any textile product are dependent on the efficiency and effectiveness of the automatic defect detection. Previously, manual human efforts are applied in textile industry to detect the defects in the fabric production process. Lack of concentration, human fatigue, and time consumption are the main drawbacks associated with the manual fabric defect detection process. Applications based on computer vision and digital image processing can address the abovementioned limitations and drawbacks. Since the last two decades, various computer vision-based applications are proposed in various research articles to address these limitations. In this review article, we aim to present a detailed study about various computer vision-based approaches with application in textile industry to detect fabric defects. The proposed study presents a detailed overview of histogram-based approaches, color-based approaches, image segmentation-based approaches, frequency domain operations, texture-based defect detection, sparse feature-based operation, image morphology operations, and recent trends of deep learning. The performance evaluation criteria for automatic fabric defect detection is also presented and discussed. The drawbacks and limitations associated with the existing published research are discussed in detail, and possible future research directions are also mentioned. This research study provides comprehensive details about computer vision and digital image processing applications to detect different types of fabric defects.

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