Fabric Defect Detection with Deep Learning and False Negative Reduction

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10 Citations (Scopus)
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Quality control is an area of utmost importance for fabric production companies. By not detecting the defects present in the fabrics, companies are at risk of losing money and reputation with a damaged product. In a traditional system, an inspection accuracy of 60-75% is observed. In order to reduce these costs, a fast and automatic defect detection system, which can be complemented with the operator decision, is proposed in this paper. To perform the task of defect detection, a custom Convolutional Neural Network (CNN) was used in this work. To obtain a well-generalized system, in the training process, more than 50 defect types were used. Additionally, as an undetected defect (False Negative - FN) usually has a higher cost to the company than a non-defective fabric being classified as a defective one (false positive), FN reduction methods were used in the proposed system. In testing, when the system was in automatic mode, an average accuracy of 75% was attained; however, if the FN reduction method was applied, with intervention of the operator, an average of 95% accuracy can be achieved. These results demonstrate the ability of the system to detect many different types of defects with good accuracy whilst being faster and computationally simple.

Original languageEnglish
Pages (from-to)81936-81945
JournalIEEE Access
Publication statusPublished - 2021


  • CNN
  • Companies
  • Computational modeling
  • Deep Learning
  • Fabric Defect Detection
  • Fabrics
  • False Negative Reduction
  • Inspection
  • Production
  • Visualization
  • Wavelet transforms


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