Sub image based eigen fabrics method using multi-class SVM classifier for the detection and classification of defects in woven fabric

Basu, Anupam; Chandra, Jayanta K.; Banerjee, Pradipta; Bhattacharyya, Saugat; Datta, Anuja

doi:10.1109/icccnt.2012.6396004

Cited by 9 publications

(7 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The study shows that our method can more effectively identify the defects than other studies. Our study achieves a 96.60% defect detection rate; Basu et al [ 31 ] achieved 96.36%, whereas other studies [ 30 , 32 ] achieved less than 95%. Our study also has a greater number of defect types than other studies.…”

Section: Discussioncontrasting

confidence: 42%

“…The defective and defect-free regions of the fabric were shown for five commonly occurring defect types. Basu et al [ 31 ] applied a principal component analysis to extract features from the training and test structure images, and a SVM classifier was used to perform the classification. The images were tested in the TILDA database of fabric defect standards; however, they were static.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated Optical Inspection for Defect Identification and Classification in Actual Woven Fabric Production Lines

Kuo

Wang

Barman

2022

Sensors

View full text Add to dashboard Cite

This paper presents a turnkey integrated system that can be operated in real time for real textile manufacturers. Eight types of defects in woven fabric, including stain, broken end, broken weft, hole, nep, double pick, kinky weft and float can be recognized and classified. First, an image is captured by a CMOS industrial camera with a pixel size of 4600 × 600 above the batcher at 20 m/min. After that, the four-stage image processing procedure is applied to detect defects and for classification. Stage 1 is image pre-processing; the filtration of the image noise is carried out by a Gaussian filter. The light source is corrected to reduce the uneven brightness resulting from halo formation. The improved mask dodging algorithm is used to reduce the standard deviation of the corrected original image. Afterwards, the background texture is filtered by an averaging filter, and the mean value is corrected for histogram shifting, so that this system is robust to the texture and color changes of woven fabric. The binary segmentation threshold is determined using the mean value and standard deviation of an image with a normal sample. Stage 2 uses adaptive binarization for separation of the background and defects and to filter the noise. In Stage 3, the morphological processing is used before the defect contour is circled, i.e., four features of each block, including the defect area, the aspect ratio of the defect, the average gray level of the defect and the defect orientation, which are calculated according to the range of contour. The image defect recognition dataset consists of 2246 images. The results show that the detection success rate is 96.44%, and the false alarm rate is 3.21%. In Stage 4, the defect classification is implemented. The support vector machine (SVM) is used for classification, 230 defect images are used as training samples, and 206 are used as test samples. The experimental results show that the overall defect recognition rate is 96.60%, providing that the software and hardware equipment designed in this study can implement defect detection and classification for woven fabric effectively.

show abstract

Section: Discussioncontrasting

confidence: 42%

Section: Introductionmentioning

confidence: 99%

Automated Optical Inspection for Defect Identification and Classification in Actual Woven Fabric Production Lines

Kuo

Wang

Barman

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Learning-based methods use labelled samples to train classifiers that distinguish between defective and non-defective samples. There are fabric defect detection studies made using classifiers such as the Support Vector Machines (SVM), 30 feedback Artificial Neural Network (ANN), 31 and the Bayes classifier (BC) 8 to learn signatures of defected and non-defected classes. But most of those pattern classification methods need a large variety of data.…”

Section: Related Workmentioning

confidence: 99%

Fabric Defect Detection Using Deep Convolution Neural Network

Fan

Wong

Wen

et al. 2021

AATCC Journal of Research

View full text Add to dashboard Cite

Fabric defect detection plays an increasingly important role in the industrial automation application for fabric production, but how to detect defects rapidly and accurately is still challenging. In this study, we propose a powerful fabric defect detection method using a hybrid of convolutional neural network (CNN) and variational autoencoder (VAE). The convolutional layers are used for extracting fabric image pattern features and the variational autoencoder is used for modeling the latent characteristics and inferring a reconstruction. The defect positions can be detected by the differences between the original image and the reconstruction image. The proposed method is validated on public patterned fabric datasets. The experimental results demonstrate that the proposed model can achieve outstanding performance in both image level and pixel level defect detection.

show abstract

“…Yapi et al 12 proposed using supervised learning to classify defective and flawless fabrics. Basu et al 13 studied fabric defect detection through a support vector machine (SVM) and other classifiers. Han and Xu 14 developed a template matching method from the statistical data of fabric textures and combined it with threshold method to detect small defects.…”

Section: Introductionmentioning

confidence: 99%

Fabric defect detection based on transfer learning and improved Faster R-CNN

Zhao

Shi

Zheng

et al. 2022

Journal of Engineered Fibers and Fabrics

View full text Add to dashboard Cite

Fabric defect detection is an important quality inspection process in the textile industry. A fabric defect detection system based on transfer learning and an improved Faster R-CNN is proposed to solve the problems of low detection accuracy, general convergence ability, and poor detection effect for small target defects in existing fabric defect detection algorithms. The pre-trained weights on the big dataset Imagenet are first extracted for transfer learning. Images are then input into the improved Faster R-CNN network, while the ResNet50 and ROI Align are used to replace the original VGG16 feature extraction network structure and a region of interest (ROI) pooling layer to avoid the problems of region mismatch caused by two quantizations from ROI pooling. The region proposal network (RPN) is combined with the multi-scale feature pyramid FPN to generate candidate regions with richer semantic information and project them onto the feature map to obtain the corresponding feature matrix. Cascaded modules are integrated and different IoU thresholds are used for each level to distinguish positive and negative samples. Finally, the softmax classifier is adopted to identify the image and obtain the predictions. The experimental results show that the detection accuracy and convergence ability of the improved Faster R-CNN are greatly enhanced compared with the current mainstream models, which provides a reference for future fabric defect detection methods.

show abstract

Sub image based eigen fabrics method using multi-class SVM classifier for the detection and classification of defects in woven fabric

Cited by 9 publications

References 12 publications

Automated Optical Inspection for Defect Identification and Classification in Actual Woven Fabric Production Lines

Automated Optical Inspection for Defect Identification and Classification in Actual Woven Fabric Production Lines

Fabric Defect Detection Using Deep Convolution Neural Network

Fabric defect detection based on transfer learning and improved Faster R-CNN

Contact Info

Product

Resources

About