DCSFPSS assisted morphological approach for grey twill fabric defect detection and defect area measurement for fabric grading

2011 ◽  
Author(s):  
V. Jayashree ◽  
S. Subbaraman
Keyword(s):  
Defect Detection ◽  
Area Measurement ◽  
Fabric Defect Detection ◽  
Defect Area ◽  
Fabric Defect ◽  
Morphological Approach

Segmentation of Fabric Defect Images Based on PCNN Model and Symmetric Tsallis Cross Entropy

2013 ◽  
Vol 760-762 ◽  
pp. 1472-1476 ◽  
Author(s):  
Hong Wan ◽  
Yi Quan Wu ◽  
Zhao Qing Cao ◽  
Zhi Long Ye
Keyword(s):  
Defect Detection ◽  
Cross Entropy ◽  
Segmentation Method ◽  
Fabric Defect Detection ◽  
Segmentation Methods ◽  
Otsu Method ◽  
Defect Area ◽  
Fabric Defect ◽  
Iteration Number ◽  
Strength Difference

Segmentation of defect images is an important step in the automatic fabric defect detection. In order to extract fabric defects effectively, a segmentation method of fabric defect images based on pulse coupled neural network (PCNN) model and symmetric Tsallis cross entropy is proposed. The image is segmented by PCNN according to the gray strength difference between fabric defect area and non-defect area. To guarantee that the grayscale inside the object and background is uniform after segmentation, symmetric Tsallis cross entropy is used as the image segmentation criterion to select the optimal threshold and iteration number. A large number of experimental results show that, compared with the related segmentation methods such as Otsu method, PCNN method, the method based on PCNN and cross entropy, the segmentation effect of the proposed method is the best. The texture of non-defect area is removed more completely, and the defect area is segmented more accurately.

Machine Vision Window Division on Self-Adaptation

2011 ◽  
Vol 460-461 ◽  
pp. 617-620
Author(s):  
Xiu Chen Wang
Keyword(s):  
Image Analysis ◽  
Machine Vision ◽  
Defect Detection ◽  
Actual Situation ◽  
Adaptive Window ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Self Adaptation ◽  
Recognition Efficiency ◽  
Division Model

Aiming at time-consuming and ineffective problem of image window division in fabric defect detection, this paper proposes a new adaptive division method after a large number of experiments. This method can quickly and exactly recognize defect feature. Firstly, a division model on adaptive window is established, secondly, the formula to anticipate generally situation of fabric image is given according to the peaks and valleys change in the model, and methods to calculate the division size and position of adaptive window are given. Finally, we conclude that the algorithm in this paper can quickly and simply select the size and position of window division according to actual situation of different fabric images, and the time of image analysis is shortened and the recognition efficiency is improved.

A Novel Fabric Defect Detection Network Based on Attention Mechanism and Multi-Task Fusion

2021 ◽  
Author(s):  
Zhengrui Peng ◽  
Xinyi Gong ◽  
Zhenfeng Lu ◽  
Xiangyi Xu ◽  
Bengang Wei ◽  
...  
Keyword(s):  
Defect Detection ◽  
Attention Mechanism ◽  
Fabric Defect Detection ◽  
Fabric Defect
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