Automatic Unsupervised Fabric Defect Detection Based on Self-Feature Comparison

Article Materials Science, Textiles

Mobile-Unet: An efficient convolutional neural network for fabric defect detection

Junfeng Jing et al.

Summary: Deep learning-based fabric defect detection methods have great potential in improving production efficiency and product quality. However, challenges such as real-time performance and data imbalance need to be addressed. To overcome these challenges, an efficient convolutional neural network, Mobile-Unet, is proposed. It utilizes the median frequency balancing loss function and depth-wise separable convolution to improve the accuracy and speed of defect segmentation.

TEXTILE RESEARCH JOURNAL (2022)

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Article Computer Science, Artificial Intelligence

Automatic fabric defect detection using a wide-and-light network

Jun Wu et al.

Summary: This paper proposes a wide-and-light network structure based on Faster R-CNN to improve the accuracy and reduce the computational cost of fabric defect detection. By designing dilated convolution modules and optimizing feature extraction networks, high-precision fabric defect detection is achieved.

APPLIED INTELLIGENCE (2021)

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Article Computer Science, Artificial Intelligence

The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection

Paul Bergmann et al.

Summary: The detection of anomalous structures in natural image data is crucial for various tasks in the field of computer vision. Unsupervised anomaly detection methods require data for training and evaluating new approaches. The MVTec anomaly detection dataset includes high-resolution color images of different objects and textures, with normal and abnormal images for training and testing purposes.

INTERNATIONAL JOURNAL OF COMPUTER VISION (2021)

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Article Computer Science, Information Systems

Fabric defect detection via low-rank decomposition with gradient information and structured graph algorithm

Boshan Shi et al.

Summary: This paper proposes a fabric defect detection method based on low rank decomposition and structured graph algorithm, which solves the two shortcomings of existing models. Evaluation on a standard database shows that this method outperforms the four latest methods in terms of total TPR and fpr.

INFORMATION SCIENCES (2021)

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Article Chemistry, Analytical