To cope with exist fabric defect detection problems, the article proposes a novel fabric defect detection method based on multi-source feature fusion. In the training process, both layer features and source model information are fused to enhance robustness and accuracy.
Jun 21, 2021
Mar 25, 2020 · Experimental results demonstrate the good performance in the defect detection for patterned fabric and more complex warp-knitted fabric.
Purpose This paper aims to propose a new method to solve the two problems in fabric defect detection. Current state-of-the-art industrial products defect ...
ABSTRACT. For fabric object inspection, the traditional approaches (e.g., Low rank approximation and sparse representation) have achieved the.
A novel fabric defect detection method based on multi-source feature fusion which provides technical support for real-time detection on industrial sites, ...
Fabric Defects Detection based on Multi-Sources Features Fusion. October 2019 ... Research on Fabric Defect Detection Based on Deep Fusion DenseNet-SSD Network.
Unsupervised learning method with robust feature extraction can achieve fabric defects detection within small samples. Abstract. Fabric defects detection plays ...
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The improved feature fusion structure effectively integrates multi-scale features, optimizes the processing of small-scale features, and enhances the model's ...
Missing: Sources | Show results with:Sources
Oct 9, 2021 · Abstract. Aiming to accurately detect various defects in the fabric production process, we propose a fabric defect detection algorithm based on ...
Missing: Sources | Show results with:Sources
This paper aims to propose a new method to solve the two problems in fabric defect detection. Current state-of-the-art industrial products defect detectors ...