Tactile-Based Fabric Defect Detection Using Convolutional Neural Network With Attention Mechanism

Bin Fang, Xingming Long, Fuchun Sun*, Huaping Liu, Shixin Zhang, Cheng Fang

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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This article proposes a fabric structure defect detection method based on the vision-based tactile sensor. The result will be robust by using the tactile sensor regardless of dyeing patterns which can influence the result if some other sensors are used, e.g., vision perception. It also reduces the influence of ambient light on defect detection. Therefore, the proposed method can be more robust and universal than conventional visual methods. A robotic arm equipped with the tactile sensors was used to automate and standardize the data collection process and construct fabric datasets. In addition, a convolutional neural network (CNN) integrated with attention mechanism in the channel domain was developed to detect fabric types. The proposed network employed frequency domain filtering to remove or weaken the influence of normal fabric texture information to improve defect detection efficiency and accuracy. Finally, several experiments were conducted to demonstrate the proposed method's superiority to a visual defect detection method for detecting structural defects. In addition, the efficiency of the proposed method is evaluated. Experimental results show that the proposed method is feasible and efficient to meet the real-world detection requirements.

Original languageEnglish
JournalIEEE Transactions on Instrumentation and Measurement
Number of pages9
Publication statusPublished - 2022


  • Attention mechanism
  • defect detection
  • vision-based tactile sensor


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