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Phased Noise Enhanced Multiple Feature Discrimination Network for fabric defect detection

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 149卷

作者： Ma, Haoran Li, Zuoyong Fan, Haoyi Zheng, Xiangpan Yan, Jiaquan Hu, Rong Fujian Univ Technol Sch Comp Sci & Math Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Peoples R China Minjiang Univ Sch Comp & Big Data Fujian Prov Key Lab Informat Proc & Intelligent Co Fuzhou 350121 Peoples R China Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou 450001 Peoples R China Minjiang Univ Coll Phys & Elect Informat Engn Fuzhou 350121 Peoples R China Wuyi Univ Key Lab Cognit Comp & Intelligent Informat Proc Fujian Educ Inst Wuyishan 354300 Peoples R China

fabric defect detection is crucial for evaluating the quality of textile products. However, the subtlety and scarcity of fabric defects pose challenges to the task of detecting. Therefore, we propose a Phased Noise Enhanced Multiple Feature Discrimination Network, which is based on phased noise enhancement strategy and multiple feature discrimination module to improve the model's ability to identify complex and subtle flaws. Specifically, we propose the phased noise enhancement strategy in the feature space to simulate feature-level anomalies that are closer to reality. This strategy can improve the input quality of the feature reconstructor, so that helps its perception and reconstruction ability. Then, we propose the multiple feature discrimination module, which has dual feature branches to improve its ability to distinguish more complex detailed texture features. In addition, we propose a subsampling module to reduce feature redundancy and ensure efficient inference speed. Finally, we conduct extensive experiments and ablation studies on two publicly available fabric datasets, AITEX and Kaggle fabric. The experimental results show that the proposed method achieved 92% and 100% image level metrics and 97.5% and 67.1% pixel level metrics on two datasets, respectively, which is superior to the current state-of-the-art methods. In addition, our method also demonstrated significant performance in generalization experiments.

关键词： fabric defect detection Generative adversarial network Anomaly detection

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Enhanced fabric defect detection With Feature Contrast Interference Suppression

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Wang, Jinbao Cheng, Jiayi Gao, Can Zhou, Jie Shen, Linlin Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Shenzhen Univ Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518060 Peoples R China Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China

Detecting fabric defects is a critical task in the textile industry, as it involves identifying and localizing imperfections in fabrics. However, many existing unsupervised defect detection methods are sensitive to variations and disturbances in the normal background of fabric images, leading to a high false alarm rate and unsatisfactory performance. To address this issue, we propose a novel defect detection method called feature contrast interference suppression (FCIS) that aims to enlarge the feature distance between foreground and background textures while suppressing the distribution of background features to obtain a more robust background representation. Our approach consists of several key components: First, we introduce background-foreground contrastive learning (BFCL) to enhance feature representation between background and foreground elements at the feature level. Second, we use multiscale denoising knowledge distillation to aid the model in learning more accurate representations of normal background features. Finally, we design an attention decoder that refines and segments the defect mask generated through feature comparison, resulting in improved detection accuracy. Our proposed FCIS has been validated using five mainstream datasets. Experimental results demonstrate that our proposed method outperforms existing approaches, achieving improvements of 2.38% in image-level AUROC and 3.73% in pixel-level AUPRO, along with a 9.12% enhancement in average defect localization precision.

关键词： Feature extraction fabrics defect detection Image reconstruction Training Accuracy Noise reduction Data models Decoding Attention segmentation fabric defect detection interference suppression knowledge distillation unsupervised contrastive learning unsupervised contrastive learning

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DF-YOLO: An attempt on enhancing generalization in fabric defect detection based on YOLO network

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TEXTILE RESEARCH JOURNAL 2025年第7-8期95卷 748-766页

作者： Gu, Mengshang Zhou, Jian Pan, Ruru Gao, Weidong Jiangnan Univ Key Lab Ecotext Wuxi Peoples R China

This article introduces Domain-fusion YOLO (DF-YOLO), a novel object detection network enhancing YOLOv5 series networks' generalizability, particularly in fabric defect detection. DF-YOLO incorporates a unique feature extractor in its backbone, enhancing frequency domain feature perception alongside spatial information. Utilizing Fourier transform, the network better discerns fabric textures and anomalies, addressing traditional convolutional neural networks' limited receptive field issue. Feature fusion and dimension reduction are applied for capturing latent features. In its neck, DF-YOLO integrates an improved BiFPN-based feature-fusion structure and CBAM attention mechanism, optimizing feature selection and fusion across scales. This mitigates information loss due to varying input resolutions and architecture depth. DF-YOLO marks a significant advancement in textile anomaly detection, improving generalization while maintaining efficiency. Its innovative approach benefits automated quality control, offering a sophisticated tool for industries requiring precise fabric inspection.

关键词： fabric defect detection anomaly detection frequency feature Fourier transform YOLOv5 DF-YOLO generalization enhancing

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A Real-Time fabric defect detection Method Based on Improved YOLOv8

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APPLIED SCIENCES-BASEL 2025年第6期15卷 3228-3228页

作者： Jin, Yanxia Liu, Xinyu Nan, Keliang Wang, Songsong Wang, Ting Zhang, Zhuangwei Zhang, Xiaozhu North Univ China Sch Comp Sci & Technol Taiyuan 030051 Peoples R China

fabric defect detection is a crucial step in ensuring product quality within the textile industry. However, current detection methods face challenges in processing efficiency for high-resolution images, detail recovery during upsampling, and the adaptability of loss functions for low-quality samples, which limit detection accuracy and real-time performance. To overcome these limitations, this paper introduces an improved YOLOv8-based model that optimizes both aspects for fabric defect detection. First, we introduce an efficient RG-C2f module to improve processing speed for high-resolution images. Second, the DySample upsampling operator is adopted to enhance edge and detail preservation, improving detail recovery within defect regions. Finally, an adaptive inner-WIoU loss function is designed to dynamically adjust focus on low-quality samples, thereby strengthening the model's generalization capability. Experimental results validated on the TILDA and Tianchi datasets show that, compared with YOLOv8, the proposed model achieves mAP improvements of 6.4% and 1.5%, respectively, demonstrating significant enhancements in detection accuracy and speed. This advancement provides strong support for fabric defect detection.

关键词： fabric defect detection object detection YOLOv8 deep learning

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Open-Set fabric defect detection With defect Generation and Transfer

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Gao, Can Chen, Xiujian Zhou, Jie Wang, Jinbao Shen, Linlin Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Peoples R China

fabric defect detection is indispensable but challenging due to the diversity of fabric texture and defect types in textile mills, and a variety of deep learning-based supervised methods have been introduced to defect detection and segmentation. However, these methods suffer from the scarcity of defective samples and are ineffective in dealing with unknown types of defects. In this study, a novel open-set fabric defect detection method is proposed. To alleviate the problem of scarcity of defective samples, a defect generation and transfer (DGT) module is first designed to generate a large number of defective samples with different defect shapes and types. Then, a defect detection module is constructed, which learns the discriminant representations from real and generated defective samples to identify and segment defects. Finally, an open-set defect classification (DFC) module is introduced, which fits the key features of each known defect type with the Weibull distribution and realizes the classification of known defect types as well as the rejection of unknown defect types. Extensive experiments conducted on five fabric datasets show that the proposed method outperforms other state-of-the-art methods, achieving an average detection performance improvement of 3.0% and 1.4% for image- and pixel-level area under the receiver operating characteristic curve (AUROC), respectively, and an average classification improvement of 8.2% and 17.2% for closed-set and open-set DFC, respectively.

关键词： fabrics defect detection Image segmentation Training Feature extraction Image synthesis Data models Inspection Training data Labeling defect generation fabric defect detection mask generator open-set classification Weibull distribution

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Semi-supervised Lightweight fabric defect detection 7th

Semi-supervised Lightweight Fabric Defect Detection

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7th Chinese Conference on Pattern Recognition and Computer Vision

作者： Dong, Xiaoliang Liu, Hao Luo, Yuexin Yan, Yubao Liang, Jiuzhen Changzhou Univ Changzhou 213164 Peoples R China

ISBN: (纸本)9789819785049;9789819785056

fabric defect detection can greatly enhance the quality of fabric production. However, the high cost of annotating defects and the computational complexity of networks are the main challenges in defect detection. To address these challenges, this paper proposes a semi-supervised lightweight fabric defect detection algorithm (SDA-Net). During the semi-supervised training process, the algorithm uses labeled defect samples and normal samples to learn latent features and detect defect positions accurately. First, to solve the issue of insufficient labeled defect samples due to high annotation costs, a data augmentation method called Sel-fill is proposed. The Sel-fill randomly samples image blocks of various sizes from a truncated normal distribution. These image blocks are then inserted into random positions within normal images, thereby generating labeled defect samples. Second, A lightweight neural network architecture is constructed using depth-wise separable convolution (DSConv). This architecture effectively reduces the number of parameters and computations while maintaining performance. Final, the max pooling coordinate attention mechanism (MpCA) effectively suppresses background noise during the multi-scale feature fusion process, resulting in improved detection precision. By using depth-wise separable convolution and MpCA attention, SDA-Net achieves an average detection precision of 62.6%, improved by 4.5% over the previous method. The number of trainable parameters is only 9.35 MB, reduced by 42.53%. Moreover, the computations are reduced by 68.84%.

关键词： fabric defect detection Sel-fill Semi-supervised Lightweight

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DGHR-YOLO: fabric defect detection based on High-level Screening -feature Pyramid Networks and deformable convolution

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NONDESTRUCTIVE TESTING AND EVALUATION 2025年

作者： Zhang, Zhixing Huang, Tongyuan Zhang, Weifeng Yang, Yihan Yu, Qianjiang Chongqing Univ Technol Sch Artificial Intelligence Chongqing Peoples R China

fabric defect detection is crucial for quality control in fabric manufacturing but remains a challenge due to the multi-scale characteristics of defects and their integration with the fabric background. To address this, we propose an efficient fabric defect detection model, DGHR-YOLO. First, the deformable convolution block (DCB) is introduced into the backbone network, leveraging its dynamic receptive field to effectively capture defect morphology and enhance feature extraction. Secondly, we propose the GS-SPPF module, designed to mitigate semantic information loss, optimize feature fusion, and improve both accuracy and inference speed. Thirdly, a lightweight High-level Screening Feature Pyramid Network (HS-FPN) is introduced, enabling effective multi-scale feature fusion while maintaining low complexity. Finally, a one-shot aggregation module based on channel shuffle and re-parameterized convolution is introduced, enhancing feature interaction across scales. Experimental results on the Tianchi textile dataset demonstrate that DGHR-YOLO achieves mAP0.5 and mAP0.5:0.95 scores of 85.8% and 72.7%, with respective improvements of 2.7% and 3.8% over YOLOv8m, while maintaining low parameter count and computational complexity.

关键词： Convolutional neural network deformable convolution network GSConv High-level Screening-feature Pyramid Networks RCS-OSA fabric defect detection

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A study on lightweight algorithms for fabric defect detection

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TEXTILE RESEARCH JOURNAL 2025年

作者： Dai, Ning Hu, Xiaohan Xu, Kaixin Hu, Xudong Yuan, Yanhong Xu, Yushan Zhejiang Sci Tech Univ 928 2nd St Xiasha Higher Educ Pk Hangzhou 310018 Peoples R China Zhejiang Kangli Automat Technol Co Ltd Hangzhou Peoples R China

In industrial applications where device capacity, computational performance, and thermal management are limited, we propose the YOLOvT-Light model for fabric defect detection. This model incorporates the convolutional block attention module (CBAM)-EfficientNet backbone network, balancing detection speed and precision while significantly reducing complexity and maintaining high precision. GhostConv replaces standard convolution in the neck section, effectively reducing parameters and computational cost through simple linear transformations. Additionally, the integration of Faster Block and C2f modules retains local feature fusion capabilities while further decreasing parameters and computation. Experimental results using the DAGM2007 dataset demonstrate that YOLOvT-Light significantly reduces weight size (9.50 MB), computation performance (13.9 Gflops), and parameter count (6.11 M) compared with the baseline model, while improving inference speed (223 fps), without sacrificing precision. This lightweight architecture ensures the feasibility of deploying the model on resource-constrained devices, making it suitable for real-time, cost-effective, and safe defect detection in textile manufacturing environments. This study provides a reliable solution for developing efficient, lightweight detection models applicable to real-world industrial settings.

关键词： Lightweight FasterNet GhostNet CBAM-EfficientNet fabric defect detection YOLO

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fabric defect detection via feature fusion and total variation regularized low-rank decomposition

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第1期83卷 609-633页

作者： Zhao, Hongling Wang, Junpu Li, Chunlei Liu, Pengcheng Yang, Ruimin ZhengZhou Univ Coll Distance Learning Zhengzhou 450007 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Peoples R China Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450007 Peoples R China Univ York Dept Comp Sci York YO105DD England

fabric defect detection plays a key role in quality control for textile products. Existing methods based on traditional image processing techniques suffer from low detection accuracy and poor adaptability. The low-rank decomposition model is able to decompose the images into sparse parts (defects) and low-rank parts (background), thus can be applied to fabric defect detection. However, such model is likely to maintain more sparse noises in the sparse parts, resulting in high false-positive rate. Meanwhile, it is difficult to comprehensively represent the complicated texture feature of fabric images using traditional single hand-crafted feature descriptors. To remedy the above two challenges, a novel fabric defect detection algorithm based on feature fusion and total variation regularized low-rank decomposition is proposed in this paper. Specifically, the first-order and second-order gradient features are extracted with a biologically inspired model, and then fused using Canonical Correlation Analysis (CCA) to more sufficiently characterize the fabric texture. Next, total variation regularization term is incorporated in low-rank decomposition model to separate fabric images into redundant background and sparse parts with less noise. Finally, the defects are inspected by segmenting the saliency map obtained from sparse matrix using threshold segmentation. The experiment results conducted on two datasets demonstrate that the proposed method has better performance and generalization comparing to the competing methods.

关键词： fabric defect detection Feature fusion Canonical correlation analysis Low-rank decomposition Total variation regularization

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fabric defect detection Based on Biological Vision Modeling

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IEEE ACCESS 2018年 6卷 27659-27670页

作者： Li, Chunlei Gao, Guangshuai Liu, Zhoufeng Yu, Miao Huang, Di Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450007 Henan Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China

fabric defect detection plays a key role in the quality control of textiles. Existing fabric defect detection methods adopt traditional pattern recognition methods;however, these methods lack adaptability and present poor detection performance. Because biological vision system has the ability to quickly locate salient objects, we propose a novel fabric defect detection algorithm based on biological vision modeling by simulating the mechanism of biological visual perception. First, a distinct, efficient, and robust feature descriptor from the biological modeling of P ganglion cells, which was proposed in our previous work, is adopted to improve the representation of fabric images with complex textures. To account for the low-rank and sparsity characteristics of biological vision, the low-rank representation (LRR) technique is adopted to model biological visual saliency, and it can decompose the fabric image into backgrounds and salient defect objects. Meanwhile, dictionary learning and Laplacian regularization are integrated into the LRR model as follows: 1) dictionary learning is used to denoise the saliency map;and 2) Laplacian regularization enlarges the gaps between defective regions and the background. Finally, the linearized accelerated direction method with adaptive penalty is adopted to solve the proposed model. The experimental results emphasize that the proposed algorithm has good detection performance for plain or twill fabrics with simple textures as well as for patterned fabrics with complex textures. Moreover, the proposed method is superior to the state-of-the-art methods in terms of its adaptability and detection efficiency.

关键词： fabric defect detection biological vision modeling image representation dictionary learning low-rank representation

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