Automatic Defect Detection of Print Fabric Using Convolutional Neural Network

• URL: http://arxiv.org/abs/2101.00703v1
• Date: Sun, 3 Jan 2021 20:56:56 GMT
• Title: Automatic Defect Detection of Print Fabric Using Convolutional Neural Network
• Authors: Samit Chakraborty, Marguerite Moore, Lisa Parrillo-Chapman
• Abstract summary: There are different contemporary research on automatic defect detection systems using image processing and machine learning techniques. Researchers have also been able to establish real-time defect detection system during weaving. This research has fulfilled this gap by developing a print fabric database and implementing deep convolutional neural network.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Automatic defect detection is a challenging task because of the variability in texture and type of fabric defects. An effective defect detection system enables manufacturers to improve the quality of processes and products. Automation across the textile manufacturing systems would reduce fabric wastage and increase profitability by saving cost and resources. There are different contemporary research on automatic defect detection systems using image processing and machine learning techniques. These techniques differ from each other based on the manufacturing processes and defect types. Researchers have also been able to establish real-time defect detection system during weaving. Although, there has been research on patterned fabric defect detection, these defects are related to weaving faults such as holes, and warp and weft defects. But, there has not been any research that is designed to detect defects that arise during such as spot and print mismatch. This research has fulfilled this gap by developing a print fabric database and implementing deep convolutional neural network (CNN).

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