Focus Generator with Score Classification on Fabric Defect Detection

Qin, Yingjie; Chen, Ming; Qi, Lizhe; Sun, Yunquan

doi:10.1109/ictai.2019.00252

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…To solve the problem of RetinaNet being suitable for only small-scale datasets, Qin et al. 12 proposed an improved RetinaNet for fabric defect classification, which includes a generation model that could compensate for the deficiency in network feature extraction. Although it performed better than the original RetinaNet, the final accuracy could not meet the industry needs.…”

Section: Related Workmentioning

confidence: 99%

Classifying fabric defects with evolving Inception v3 by improved L_2,1-norm regularized extreme learning machine

Zhou

Yang

et al. 2022

Textile Research Journal

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To improve efficiency and classification accuracy, and overcome the issue of poor generalization performance of traditional fabric defect classification methods, we present a new fabric defect classification algorithm with evolving Inception v3 by improved L2,1-norm regularized extreme learning machine. Herein, first, the features of fabric images are extracted using Inception v3, which reduces the amount of computation and improves the accuracy of feature extraction. Second, an L2,1-norm regularization extreme learning machine algorithm based on multi-verse optimizer–Henry gas solubility optimization, called MHL21-RELM, is proposed. The proposed algorithm solves the problems of the low classification accuracy of the traditional ELM and prevents the algorithm from easily falling into the local optimal solution. Next, instead of SoftMax, MHL21-RELM is used as the classification network layer of Inception V3, and a new fabric defect classification algorithm named Inv3-MHL21-RELM is proposed to enhance the fabric defect classification performance. Finally, the performance of our algorithm is verified by experiments on different fabric defect datasets. The proposed method achieves 97.29% classification accuracy with the Textile Texture Database; it achieves a 6–30% improvement in classification accuracy compared with other traditional fabric defect classification methods, thus effectively improving the classification accuracy of fabric defects. In addition, this method achieves good classification results with different types of fabric defect datasets, thereby showing strong generalization.

show abstract

Section: Related Workmentioning

confidence: 99%

Classifying fabric defects with evolving Inception v3 by improved L_2,1-norm regularized extreme learning machine

Zhou

Yang

et al. 2022

Textile Research Journal

View full text Add to dashboard Cite

show abstract

Focus Generator with Score Classification on Fabric Defect Detection

Cited by 1 publication

References 16 publications

Classifying fabric defects with evolving Inception v3 by improved L_2,1-norm regularized extreme learning machine

Classifying fabric defects with evolving Inception v3 by improved L_2,1-norm regularized extreme learning machine

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Focus Generator with Score Classification on Fabric Defect Detection

Cited by 1 publication

References 16 publications

Classifying fabric defects with evolving Inception v3 by improved L2,1-norm regularized extreme learning machine

Classifying fabric defects with evolving Inception v3 by improved L2,1-norm regularized extreme learning machine

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Classifying fabric defects with evolving Inception v3 by improved L_2,1-norm regularized extreme learning machine

Classifying fabric defects with evolving Inception v3 by improved L_2,1-norm regularized extreme learning machine