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Jiuzhen Liang

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2 papers

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EAAI Journal 2025 Journal Article

Fabric defect detection via Explicit De-Background

Yuntao Chen
Hao Liu
Jiuzhen Liang

Fabric defect detection under complex backgrounds faces challenges like high interference, false positives, and lack of robustness. To address these issues, a frequency-domain Explicit De-Background method is proposed to separate background from defects and enhance defect focus. The network uses a pixel-level De-background Layer to suppress noise and emphasize defects after extracting multi-scale features with Swin Transformer. This layer includes the Frequency-domain Background Extraction Module (F-BEM) and the Background Suppression Unit (BSU): F-BEM leverages Fourier amplitude to capture global background features, while BSU suppresses them to produce a difference map that accentuates defect regions. The De-Background Attention (DBA) module leverages the difference map as a weighting matrix to enhance spatial focus on defect features while minimizing background interference. To integrate multi-scale information, the Feature Cross-Shrinking Decoder (FCSD) progressively fuses adjacent layers via Cross Aggregation Nodes(CAN), ensuring semantic consistency, reducing redundancy, and mitigating information loss and gradient vanishing for precise defect segmentation. Our method enhances robustness and accuracy in complex backgrounds using explicit background separation and multi-stage feature processing. It surpasses state-of-the-art techniques on fabric defect datasets and shows good generalization in complex and transfer learning scenarios, providing a practical solution for industrial defect detection.

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EAAI Journal 2023 Journal Article

U-SMR: U-SwinT & multi-residual network for fabric defect detection

Hao Qu
Lan Di
Jiuzhen Liang
Hao Liu

Fabric defect detection methods based on deep networks are widely used in the textile industry, but they often suffer from poor model generalization and blurry edge detection. To resolve these challenges, we propose a novel network called “U-SMR Net”, which integrates global contextual features, defect detail features, and high-level semantic features through the combination of ResNet-50 and Swin Transformer modules. Our U-SMR network includes a lightweight multiscale feature extraction module, the dual-branch pyramid Module (DBPM), which is nested to preserve high-resolution, shallow semantic information. We propose a recursive multi-level residual decoding block for multiscale fusion to refine, filter, and enhance input characteristics, generating prediction maps at multiple stages, and by employing an improved binary cross entropy loss function to supervise saliency mapping. The experimental results based on four groups from ZJU-Leaper dataset demonstrate the superior performance of our approach compared to other competitive methods by achieving an average f m e a s u r e score of 75. 33%, and finally testing results from both ZJU-Leaper-Total dataset and the HKU-Fabric dataset further support our U-SMR Net’s validity and generalization ability.

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