Paper Info
Title
Tactile-Based Fabric Defect Detection Using Convolutional Neural Network With Attention Mechanism
Abstract
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.
Year
DOI
Venue
2022
10.1109/TIM.2022.3165254
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Keywords
DocType
Volume
Fabrics, Feature extraction, Frequency-domain analysis, Tactile sensors, Cameras, Visualization, Sensors, Attention mechanism, defect detection, vision-based tactile sensor
Journal
71
ISSN
Citations 
PageRank 
0018-9456
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Bin Fang112021.04
Xingming Long200.68
Fuchun Sun32377225.80
Huaping Liu41039107.17
Shixin Zhang500.34
Fang Cheng62610.53