Fabric defect detection based on multi-scale wavelet transform and Gaussian mixture model method

2014 ◽  
Vol 106 (6) ◽  
pp. 587-592 ◽  
Author(s):  
Pengfei Li ◽  
Huanhuan Zhang ◽  
Junfeng Jing ◽  
Renzhong Li ◽  
Juan Zhao
Keyword(s):  
Wavelet Transform ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Defect Detection ◽  
Gaussian Mixture ◽  
Multi Scale ◽  
Fabric Defect Detection ◽  
Model Method ◽  
Fabric Defect

scholarly journals Multiple-Antenna Cooperative Spectrum Sensing Based on the Wavelet Transform and Gaussian Mixture Model

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3863
Author(s):  
Shunchao Zhang ◽  
Yonghua Wang ◽  
Hantao Yuan ◽  
Pin Wan ◽  
Yongwei Zhang
Keyword(s):  
Wavelet Transform ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Spectrum Sensing ◽  
Channel Model ◽  
Cooperative Spectrum Sensing ◽  
Gaussian Mixture ◽  
Multiple Antenna ◽  
Two Dimensional ◽  
Statistical Features

Spectrum sensing is a core technology in cognitive radio (CR) systems. In this paper, a multiple-antenna cooperative spectrum sensor based on the wavelet transform and Gaussian mixture model (MAWG) is proposed. Compared with traditional methods, the MAWG method avoids the derivation of the threshold and improves the performance of single secondary user (SU) spectrum sensing in cases of channel loss and hidden terminal. The MAWG method reduces the noise of the signal which collected by the multiple-antenna SUs through the wavelet transform. Then, the fusion center (FC) extracts the statistical features from the signals that are pre-processed by the wavelet transform. To extract the statistical features, an sensing data fusion method is proposed. The MAWG method divides all SUs that are involved in the cooperative spectrum sensing into two clusters and extracts a two-dimensional feature vector. In order to avoid complicated decision threshold derivation, the Gaussian mixture model (GMM) is used to train a classifier for spectrum sensing according to these two-dimensional feature vectors. Simulation experiments are performed in the κ - μ channel model. The simulation shows that the MAWG can effectively improve spectrum sensing performance under the κ - μ channel model.

scholarly journals Automatic Fabric Defect Detection Method Using PRAN-Net

2020 ◽  
Vol 10 (23) ◽  
pp. 8434
Author(s):  
Peiran Peng ◽  
Ying Wang ◽  
Can Hao ◽  
Zhizhong Zhu ◽  
Tong Liu ◽  
...  
Keyword(s):  
Defect Detection ◽  
Detection Method ◽  
Ground Truth ◽  
Inspection Time ◽  
Feature Maps ◽  
Multi Scale ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Feature Pyramid ◽  
Quality Process

Fabric defect detection is very important in the textile quality process. Current deep learning algorithms are not effective in detecting tiny and extreme aspect ratio fabric defects. In this paper, we proposed a strong detection method, Priori Anchor Convolutional Neural Network (PRAN-Net), for fabric defect detection to improve the detection and location accuracy of fabric defects and decrease the inspection time. First, we used Feature Pyramid Network (FPN) by selected multi-scale feature maps to reserve more detailed information of tiny defects. Secondly, we proposed a trick to generate sparse priori anchors based on fabric defects ground truth boxes instead of fixed anchors to locate extreme defects more accurately and efficiently. Finally, a classification network is used to classify and refine the position of the fabric defects. The method was validated on two self-made fabric datasets. Experimental results indicate that our method significantly improved the accuracy and efficiency of detecting fabric defects and is more suitable to the automatic fabric defect detection.

Fully Automated Fabric Defect Detection Using Additive Wavelet Transform

2020 ◽  
Vol 29 (2) ◽  
pp. 119-125
Author(s):  
Eman Hussein Saleh ◽  
Mohamed Mohamed Fouad ◽  
Mohamed S. Sayed ◽  
Wael Badawy ◽  
Fathi E. Abd El-Samie
Keyword(s):  
Wavelet Transform ◽  
Defect Detection ◽  
Fabric Defect Detection ◽  
Fabric Defect
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