The Experts below are selected from a list of 31533 Experts worldwide ranked by ideXlab platform
Ajay Kumar - One of the best experts on this subject based on the ideXlab platform.
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Computer-vision-based fabric Defect Detection: A survey
IEEE Transactions on Industrial Electronics, 2008Co-Authors: Ajay KumarAbstract:The investment in an automated fabric Defect Detection system is more than economical when reduction in labor cost and associated benefits are considered. The development of a fully automated web inspection system requires robust and efficient fabric Defect Detection algorithms. The inspection of real fabric Defects is particularly challenging due to the large number of fabric Defect classes, which are characterized by their vagueness and ambiguity. Numerous techniques have been developed to detect fabric Defects and the purpose of this paper is to categorize and/or describe these algorithms. This paper attempts to present the first survey on fabric Defect Detection techniques presented in about 160 references. Categorization of fabric Defect Detection techniques is useful in evaluating the qualities of identified features. The characterization of real fabric surfaces using their structure and primitive set has not yet been successful. Therefore, on the basis of the nature of features from the fabric surfaces, the proposed approaches have been characterized into three categories; statistical, spectral and model-based. In order to evaluate the state-of-the-art, the limitations of several promising techniques are identified and performances are analyzed in the context of their demonstrated results and intended application. The conclusions from this paper also suggest that the combination of statistical, spectral and model-based approaches can give better results than any single approach, and is suggested for further research.
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Defect Detection in textured materials using gabor filters
IEEE Industry Applications Society Annual Meeting, 2000Co-Authors: Ajay Kumar, Grantham K H PangAbstract:This paper investigates various approaches for automated inspection of textured materials using Gabor wavelet features. A new supervised Defect Detection approach to detect a class of Defects in textile webs is proposed. Unsupervised web inspection using a multichannel filtering scheme is investigated. A new data fusion scheme to multiplex the information from the different channels is proposed. Various factors interacting the tradeoff for performance and computational load are discussed. This scheme establishes high computational savings over the previously proposed approaches and results in high quality of Defect Detection. Final acceptance of visual inspection systems depends on economical aspects as well. Therefore, a new low-cost solution for fast web inspection is also included in this paper. The experimental results conducted on real fabric Defects for various approaches proposed in this paper confirm their usefulness.
S P Yung - One of the best experts on this subject based on the ideXlab platform.
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wavelet based methods on patterned fabric Defect Detection
Pattern Recognition, 2005Co-Authors: Henry Y T Ngan, Grantham K H Pang, S P YungAbstract:The wavelet transform (WT) has been developed over 20 years and successfully applied in Defect Detection on plain (unpatterned) fabric. This paper is on the use of the wavelet transform to develop an automated visual inspection method for Defect Detection on patterned fabric. A method called direct thresholding (DT) based on WT detailed subimages has been developed. The golden image subtraction method (GIS) is also introduced. GIS is an efficient and fast method, which can segment out the Defective regions on patterned fabric effectively. In this paper, the method of wavelet preprocessed golden image subtraction (WGIS) has been developed for Defect Detection on patterned fabric or repetitive patterned texture. This paper also presents a comparison of the three methods. It can be concluded that the WGIS method provides the best Detection result. The overall Detection success rate is 96.7% with 30 Defect-free images and 30 Defective patterned images for one common kind of patterned Jacquard fabric.
Wei Fang - One of the best experts on this subject based on the ideXlab platform.
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An Effective Method of Weld Defect Detection and Classification Based on Machine Vision
IEEE Transactions on Industrial Informatics, 2019Co-Authors: Chao Li, Xiao-jun Wu, Vasile Palade, Wei FangAbstract:In order to effectively identify and classify weld Defects of thin-walled metal canisters, a weld Defect Detection and classification algorithm based on machine vision is proposed in this paper. With the weld Defects categorized, a modified background subtraction method based on Gaussian mixture models, is proposed to extract the feature areas of the weld Defects. Then, we design an algorithm for weld Detection and classification according to the extracted features. Next, by using the weld images sampled by the constructed weld Defect Detection system on a real-world production line, the parameters of the weld Defect classifiers are determined empirically. Experimental results show that the proposed methods can identify and classify the weld Defects with more than 95% accuracy rate. Moreover, the weld Detection results obtained in the actual production line show that the Detection and classification accuracy can reach more than 99%, which means that the system enhanced with the proposed method can meet the requirements for the best real-time and continuous weld Defect Detection systems available nowadays.
Chaoqun Zhang - One of the best experts on this subject based on the ideXlab platform.
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weld Defect Detection based on deep learning method
Conference on Automation Science and Engineering, 2019Co-Authors: Haodong Zhang, Zuzhi Chen, Chaoqun ZhangAbstract:Welding is an important joining technology but the Defects in welds wreck the quality of the product evidently. Due to the variety of weld Defects’ characteristics, weld Defect Detection is a complex task in industry. In this paper, we try to explore a possible solution for weld Defect Detection and a novel image-based approach is proposed using small X-ray image data sets. An image-processing based data augmentation approach and a WGAN based data augmentation approach are applied to deal with imbalanced image sets. Then we train two deep convolutional neural networks (CNNs) on the augmented image sets using feature-extraction based transfer learning techniques. The two trained CNNs are combined to classify Defects through a multi-model ensemble framework, aiming at lower false Detection rate. Both of the experiments on augmented images and real world Defect images achieve satisfying accuracy, which substantiates the possibility that the proposed approach is promising for weld Defect Detection.
Jiahao Pan - One of the best experts on this subject based on the ideXlab platform.
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deformable patterned fabric Defect Detection with fisher criterion based deep learning
IEEE Transactions on Automation Science and Engineering, 2017Co-Authors: Weigang Zhao, Jiahao PanAbstract:In this paper, we propose a discriminative representation for patterned fabric Defect Detection when only limited negative samples are available. Fabric patches are efficiently classified into Defectless and Defective categories by Fisher criterion-based stacked denoising autoencoders (FCSDA). First, fabric images are divided into patches of the same size, and both Defective and Defectless samples are utilized to train FCSDA. Second, test patches are classified through FCSDA into Defective and Defectless categories. Finally, the residual between the reconstructed image and Defective patch is calculated, and the Defect is located by thresholding. Experimental results demonstrate the effectiveness of the proposed scheme in the Defect Detection for periodic patterned fabric and more complex jacquard warp-knitted fabric.
