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

doi:10.1109/TIM.2022.3165254

	Tactile-Based Fabric Defect Detection Using Convolutional Neural Network With Attention Mechanism
	Bin Fang 1; Long, Xingming 2; Sun, Fuchun 1; Liu, Huaping 1; Zhang, Shixin 3; Fang, Cheng 4
	2022
发表期刊	IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
ISSN	0018-9456
卷号	71 页码:9
摘要	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.
关键词	Fabrics Feature extraction Frequency-domain analysis Tactile sensors Cameras Visualization Sensors Attention mechanism defect detection vision-based tactile sensor
DOI	10.1109/TIM.2022.3165254
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62173197] ; Beijing Science Technology Project[Z191100008019008]
WOS研究方向	Engineering ; Instruments & Instrumentation
WOS类目	Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:000795104800001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：36[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19563
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Sun, Fuchun
作者单位	1.Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Inst Artificial Intelligence, State Key Lab Intelligent Technol & Syst,Dept Com, Beijing 100084, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100086, Peoples R China 3.China Univ Geosci Beijing, Sch Engn & Technol, Beijing 100083, Peoples R China 4.Univ Southern Denmark, Maersk McKinney Moller Inst, DK-5230 Odense, Denmark
推荐引用方式 GB/T 7714	Bin Fang,Long, Xingming,Sun, Fuchun,et al. Tactile-Based Fabric Defect Detection Using Convolutional Neural Network With Attention Mechanism[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2022,71:9.
APA	Bin Fang,Long, Xingming,Sun, Fuchun,Liu, Huaping,Zhang, Shixin,&Fang, Cheng.(2022).Tactile-Based Fabric Defect Detection Using Convolutional Neural Network With Attention Mechanism.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,71,9.
MLA	Bin Fang,et al."Tactile-Based Fabric Defect Detection Using Convolutional Neural Network With Attention Mechanism".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 71(2022):9.