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REVIEW 5 major objections 4 minor 35 references

Fab-ME: A Vision State-Space and Attention-Enhanced Framework for Fabric Defect Detection

T0 review · 5 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read This paper proposes Fab-ME, a YOLOv8s variant with C2F-VMamba and EMCA modules, reaching 59.4% mAP@0.5 on the Tianchi fabric defect dataset, a 3.5% relative gain over the YOLOv8s baseline.

desk verdict Plausible engineering gain, unproven: single-seed numbers and a defect-only validation set leave the 3.5% mAP improvement inside noise. read the letter →

arxiv 2412.03200 v2 pith:7E6WDYFI submitted 2024-12-04 cs.CV

Shuai Wang , Huiyan Kong , Baotian Li , Fa Zheng This is my paper · ORCID
classification cs.CV
keywords fabricdefectdetectionYOLOv8svisionstate-spacemodelVMambachannelattentionreal-timeobjectmulti-scalefeatureextractionTianchidataset
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper is trying to show that a lightweight modification of YOLOv8s can detect fabric defects more accurately without sacrificing speed. The proposed Fab-ME builds global context into the neck with C2F-VMamba, a module that inserts vision state-space blocks into YOLOv8s' C2F block, and improves small-target sensitivity with EMCA, a multi-scale channel attention placed after SPPF. On the Tianchi 20-class fabric defect dataset, Fab-ME reaches 59.4% mAP@0.5, beating the 57.4% YOLOv8s baseline by 3.5% relative, while keeping about 11 million parameters and real-time speed. If correct, it offers a practical recipe for industrial fabric inspection, where defects are long, narrow, and varied.

What carries the argument

The machinery is the C2F-VMamba module and the EMCA module. C2F-VMamba takes the YOLOv8s C2F, the cross-stage partial bottleneck with two convolutions, and routes one split through stacked VSS blocks; each VSS block splits again, sends one branch through a depthwise convolution and the other through a 2D selective scan (SS2D), then merges them. EMCA computes channel weights from the sum of global average pooling and global max pooling passed through a 1D convolution with kernel size 5 and a sigmoid, then multiplies each channel. The first supplies a global receptive field at linear complexity, and the second recalibrates channels with multi-scale descriptors.

What would settle it

Run both YOLOv8s and Fab-ME from the same codebase on the identical tiled Tianchi train/validation split with the same hyperparameters and multiple random seeds; if the average mAP@0.5 gap is not close to 2.0 absolute points, or if confidence intervals overlap, the central claim fails.

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Extended reading notes

Core claim

Fab-ME is a YOLOv8s detector in which the third C2F block in the neck is replaced by C2F-VMamba, a C2F module whose split path runs through stacked visual state-space (VSS) blocks built on 2D selective scanning (SS2D), and in which the SPPF output of the backbone is followed by EMCA, a channel attention that sums global average and global max pooling before a 1D convolution with kernel size 5 and a sigmoid. On the Tianchi 20-class fabric defect dataset this reaches 59.4% mAP@0.5, compared with a 57.4% YOLOv8s baseline, a 3.5% relative improvement, while parameters stay near 11.0 million. The paper credits the VSS blocks for global context and detail capture and EMCA for small-target sensitivity, with the ablation showing that replacing the third neck C2F contributes the larger share of the gain.

Load-bearing premise

The gain assumes the 57.4% YOLOv8s baseline was trained and validated under exactly the same tiled, non-defective-discarded protocol as Fab-ME; if that baseline reproduction is off, the claimed 3.5 percent improvement is not established.

Editorial extensions

If this is right

  • On the Tianchi benchmark, Fab-ME reaches 59.4% mAP@0.5, ahead of the 57.4% YOLOv8s baseline and every listed published method in the comparison table.
  • The ablation attributes the largest single gain to replacing the third C2F in the neck with C2F-VMamba, with EMCA adding a further increment.
  • The same two modules also improve YOLOv5s and YOLOv6s in the authors' experiments, so the recipe transfers across YOLO versions.
  • Parameter count stays near 11.0 million, so the added modules do not obviously cost real-time throughput.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper's evidence: the modules are not fabric-specific, so the same recipe is plausible for steel, wood, or ceramic surface-defect detection, but the paper does not test this.
  • Beyond the paper's evidence: because non-defective 640x640 tiles are discarded before training and validation, the reported numbers describe a defect-dense distribution; on untrimmed production images the gain could shrink.
  • Beyond the paper's evidence: rerunning the baseline and Fab-ME with multiple seeds and reporting variance would establish whether the 2.0 absolute-point gap is stable or within run-to-run noise.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 4 minor

Summary. The manuscript proposes Fab-ME, a modified YOLOv8s detector for fabric defect detection on the Tianchi dataset. Two architectural changes are introduced: a C2F-VMamba module that replaces one C2F block in the neck by integrating Visual State-Space (VSS) blocks, and an Enhanced Multi-scale Channel Attention (EMCA) module placed after the SPPF block. The paper reports 59.4% mAP@0.5 versus a 57.4% YOLOv8s baseline, i.e., a 3.5% relative improvement, and claims real-time processing. Ablations over insertion positions, the EMCA module, and transfer to YOLOv5s/YOLOv6s are presented.

Significance. If the reported gain is reproducible, this is a modest but potentially useful incremental contribution to textile inspection: a low-parameter YOLO variant with state-space blocks and channel attention. The paper is transparent about the dataset class distribution (Table I) and reports parameter counts. However, the central claim currently rests on a single unseeded validation split, and the evaluation protocol discards all non-defective sub-images, so the 3.5% improvement is not yet established. In addition, the real-time claim is unsupported by any speed measurement. With these gaps addressed, the contribution would be of interest to the defect-detection community.

major comments (5)
  1. [Section III-A] The validation protocol discards non-defective 640x640 sub-images before computing mAP@0.5. As a result, the validation set contains only tiles with at least one defect, so false positives on defect-free fabric are never penalized in the reported metric. Since the intended application is continuous fabric inspection, this protocol may overstate practical detection quality. Please report results on the full original test images, or at least on a keep-all-tiles split, as a sensitivity check.
  2. [Table II and Fig. 3] The central claim rests on a single comparison: Baseline 57.4 versus Fab-ME 59.4 mAP@0.5, with no standard deviation, number of seeds, or confidence interval. For modern object detectors, run-to-run variation of this magnitude is common, and the insertion positions were selected on the same validation set, which adds a selection effect. Please run multiple seeds and report mean and standard deviation, or provide a bootstrap confidence interval at minimum. Releasing code and trained weights would also make the result independently verifiable.
  3. [Abstract and Section III-E.4] The abstract and Section III-E.4 claim real-time processing, but no frames-per-second or latency measurement is reported anywhere in the paper. Since C2F-VMamba adds a 2D selective-scan operation and EMCA adds a convolution-based attention module, the speed claim needs direct support. Please measure and report inference time on the stated RTX 4090 hardware, including preprocessing time if the tiling protocol is part of deployment.
  4. [Section II-C] The EMCA module is described as 'multi-scale', but the displayed computation is a single fixed-size 1D convolution applied to the sum of two global pooling descriptors: a = sigma(Conv1D(GAP(F) + GMP(F), k)). There are no multiple kernel sizes, dilation rates, or parallel branches. Either clarify what makes this attention multi-scale, or replace the 'multi-scale' characterization with a more accurate description such as 'dual-pooling channel attention'.
  5. [Section III-E.1 and Fig. 3(a)] The text states that replacing the third C2F block gives a 'performance improvement of 2.5%'. If the values in Fig. 3(a) are read as mAP percentages, the baseline is 57.4 and the best single-position replacement is 58.9, which is 1.5 absolute points or about 2.6% relative, not 2.5%. The paper should state explicitly whether all reported percentages are absolute or relative and should reconcile the text with the figure labels.
minor comments (4)
  1. [Section II-A] The text first says EMCA is integrated 'after the SPPF block within the YOLOv8 backbone', but the following sentence says the light blue box denoting EMCA is in the 'feature fusion module neck'. Fig. 1 appears to show EMCA after a concatenation in the neck. Please clarify the actual insertion location.
  2. [Section III-F] Section III-F says 'The comparison diagram in Fig. 2 presents the predictions', but Fig. 2 is a sample display of original defect images and Fig. 5 is the visualization figure. The reference should be corrected to Fig. 5.
  3. [Section I] The introduction cites [23] for Vision Mamba and [25] for VMamba, which are distinct models with the same first-author group. The text should make clear which specific architecture is used for the VSS blocks in C2F-VMamba.
  4. [Fig. 4] Fig. 4 reports results on YOLOv5s and YOLOv6s, but the text in Section III-E does not explain the training setup or the 'Original' versus 'Ours' comparison for those backbones. Adding a sentence on the hyperparameters and evaluation split used for Fig. 4 would improve reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: Fab-ME's reported gains are measured benchmark results, not derived from fitted constants or self-citations.

full rationale

The paper reports an empirical detection benchmark rather than a derivation. The central claim, a 3.5% mAP@0.5 improvement over YOLOv8s (Table II and Fig. 3(c)), is a measured validation metric for a fixed model configuration; no parameter is fitted to a subset and then re-predicted as a 'prediction.' The C2F-VMamba and EMCA modules are defined compositions of VSS blocks [25] and ECA [28], both external prior work, and the paper does not invoke any self-citation as load-bearing evidence. The ablation in Fig. 3 selects the best of a small set of module placements, which is a mild selection effect, but it does not make the final number equal to its inputs by construction. The tiling protocol (Section III-A) discarding non-defective sub-images is an evaluation-choice concern, and the 'real-time' claim lacks FPS measurements, but these are correctness or evidence issues, not circularity. No circular step can be exhibited, so the score is 0.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central result rests on architecture choices selected via validation-set ablations and on the assumption that the tiled validation split is representative. None of these are derived from first principles; they are either tacit defaults or empirical choices made on the same split used to report the final mAP.

free parameters (4)
  • EMCA kernel size k = K=5
    The 1D convolution kernel size in the EMCA attention module is set to 5 by hand; no sensitivity analysis is given and it materially affects the attention weights.
  • Number of VSS blocks n in C2F-VMamba = unspecified
    The module description uses n sequential VSS blocks but the paper does not state n, leaving a free architectural choice needed for reproduction.
  • C2F-VMamba insertion position = third C2F in neck (C2F3)
    Ablations in Fig. 3(a) test all four neck positions and the best one is chosen; this selection is made on the validation set.
  • EMCA insertion point = after SPPF in backbone
    The module placement is a design choice; only this placement is evaluated, with no comparison to alternative positions.
assumptions (4)
  • domain assumption The Tianchi dataset annotations are correct and complete.
    All ground-truth boxes are taken as-is; no manual verification is described in Section III-A.
  • domain assumption Tiling images into 640x640 sub-images and discarding non-defective tiles preserves the defect distribution and does not bias evaluation.
    This preprocessing changes the evaluation distribution; the paper does not validate that tile-level mAP equals full-image mAP (Section III-A).
  • domain assumption The published VSS block from VMamba and ECA module are used as described, and their implementations are correct.
    The paper relies on external implementations whose correctness is not independently verified (Section II-B and II-C, references [25] and [28]).
  • domain assumption Early stopping on validation mAP is a valid model selection criterion.
    Training stops when validation performance stalls for 50 epochs; this assumes the validation metric is a reliable proxy for generalization (Section III-C).

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Cite this review

Pith. "Pith review of Fab-ME: A Vision State-Space and Attention-Enhanced Framework for Fabric Defect Detection." pith.science (2026). https://pith.science/paper/7E6WDYFI

@misc{pith2026241203200,
  author       = {Pith},
  title        = {Pith review of: Fab-ME: A Vision State-Space and Attention-Enhanced Framework for Fabric Defect Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7E6WDYFI}},
  note         = {Machine review of arXiv:2412.03200}
}
read the original abstract

Effective defect detection is critical for ensuring the quality, functionality, and economic value of textile products. However, existing methods face challenges in achieving high accuracy, real-time performance, and efficient global information extraction. To address these issues, we propose Fab-ME, an advanced framework based on YOLOv8s, specifically designed for the accurate detection of 20 fabric defect types. Our contributions include the introduction of the cross-stage partial bottleneck with two convolutions (C2F) vision state-space (C2F-VMamba) module, which integrates visual state-space (VSS) blocks into the YOLOv8s feature fusion network neck, enhancing the capture of intricate details and global context while maintaining high processing speeds. Additionally, we incorporate an enhanced multi-scale channel attention (EMCA) module into the final layer of the feature extraction network, significantly improving sensitivity to small targets. Experimental results on the Tianchi fabric defect detection dataset demonstrate that Fab-ME achieves a 3.5% improvement in mAP@0.5 compared to the original YOLOv8s, validating its effectiveness for precise and efficient fabric defect detection.

Figures

Figures reproduced from arXiv: 2412.03200 by the authors.

Figure 1
Figure 1. The proposed Fab-ME framework. The feature maps generated at each stage of the backbone network are denoted as [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Sample display of original fabric defect images from the Tianchi [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Ablation study of key components in our method. (a) Ablation [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Ablation studies of our proposed module were conducted on [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Visualization result. Compared our method with Faster R-CNN, YOLOv5s, and baseline. The numbers near the boxes in the figure represent the [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]

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Reference graph

Works this paper leans on

35 extracted references · 28 canonical work pages

  1. [1]

    Deep learning-based fabric defect detection: A review,

    Y . Kahraman and A. Durmusoglu, “Deep learning-based fabric defect detection: A review,” Textile Research Journal , vol. 93, no. 5-6, pp. 1485–1503, 2023

    work page 2023

  2. [2]

    Enhancing landslide segmentation with guide attention mechanism and fast fourier transformer,

    Kaiyu Yan, Fei Shen, and Zongyi Li, “Enhancing landslide segmentation with guide attention mechanism and fast fourier transformer,” in International Conference on Intelligent Computing . Springer, 2024, pp. 296–307

    work page 2024

  3. [3]

    Fourier-fpn: Fourier improves multi-scale feature learning for oriented tiny object detection,

    Yong Tang, Hongan Pan, Jun Guo, Fei Shen, Zhengzhou Zhu, and Honghui Jia, “Fourier-fpn: Fourier improves multi-scale feature learning for oriented tiny object detection,” in International Conference on Intelligent Computing. Springer, 2024, pp. 450–461

    work page 2024

  4. [4]

    Fabric defect detection based on transfer learning and improved Faster R-CNN,

    Zhao Jia, Zhou Shi, Zheng Quan, and Mei Shunqi, “Fabric defect detection based on transfer learning and improved Faster R-CNN,” JOURNAL OF ENGINEERED FIBERS AND FABRICS , vol. 17, 2022

    work page 2022

  5. [5]

    Enhancing aerial object detection with selective frequency interaction network,

    Weijie Weng, Mengwan Wei, Junchi Ren, and Fei Shen, “Enhancing aerial object detection with selective frequency interaction network,” IEEE Transactions on Artificial Intelligence , vol. 1, no. 01, pp. 1–12, 2024

    work page 2024

  6. [6]

    Lr-fpn: Enhancing remote sensing object detection with location refined feature pyramid network,

    Hanqian Li, Ruinan Zhang, Ye Pan, Junchi Ren, and Fei Shen, “Lr-fpn: Enhancing remote sensing object detection with location refined feature pyramid network,” arXiv preprint arXiv:2404.01614 , 2024

    arXiv 2024

  7. [7]

    FP-Deeplab: A segmentation model for fabric defect detection,

    Yu Liu, Jie Shen, Ruifan Ye, Shu Wang, Jia Ren, and Haipeng Pan, “FP-Deeplab: A segmentation model for fabric defect detection,” Mea- surement Science and Technology , vol. 35, no. 10, pp. 106008, 2024

    work page 2024

  8. [8]

    Advancing pose-guided image synthesis with progressive conditional diffusion models,

    Fei Shen, Hu Ye, Jun Zhang, Cong Wang, Xiao Han, and Yang Wei, “Advancing pose-guided image synthesis with progressive conditional diffusion models,” in The Twelfth International Conference on Learning Representations, 2023

    work page 2023

Show all 35 references
  1. [9]

    Boosting consistency in story visualization with rich-contextual conditional diffusion models,

    Fei Shen, Hu Ye, Sibo Liu, Jun Zhang, Cong Wang, Xiao Han, and Wei Yang, “Boosting consistency in story visualization with rich-contextual conditional diffusion models,” arXiv preprint arXiv:2407.02482 , 2024

    2024 arXiv

  2. [10]

    Imagdressing-v1: Customizable virtual dressing,

    Fei Shen, Xin Jiang, Xin He, Hu Ye, Cong Wang, Xiaoyu Du, Zechao Li, and Jinghui Tang, “Imagdressing-v1: Customizable virtual dressing,” arXiv preprint arXiv:2407.12705 , 2024

    2024 arXiv

  3. [11]

    Imagpose: A unified conditional framework for pose-guided person generation,

    Fei Shen and Jinhui Tang, “Imagpose: A unified conditional framework for pose-guided person generation,” in The Thirty-eighth Annual Conference on Neural Information Processing Systems , 2024

    2024

  4. [12]

    A mixed-attention-based multi-scale autoencoder algorithm for fabric de- fect detection,

    Hongwei Zhang, Yanzi Wu, Shuai Lu, Le Yao, and Pengfei Li, “A mixed-attention-based multi-scale autoencoder algorithm for fabric de- fect detection,” Coloration Technology, vol. 140, no. 3, pp. 451–466, 2023

    2023

  5. [13]

    Knowledge distillation for unsupervised defect detection of yarn-dyed fabric using the system DAERD: Dual attention embedded reconstruc- tion distillation,

    Hongwei Zhang, Shuaibo Liu, Shuai Lu, Le Yao, and Pengfei Li, “Knowledge distillation for unsupervised defect detection of yarn-dyed fabric using the system DAERD: Dual attention embedded reconstruc- tion distillation,” Coloration Technology, vol. 140, no. 1, pp. 125–143, 2024

    2024

  6. [14]

    Automated fabric defect detection using multi-scale fusion MemAE,

    Kun Wu, Lei Zhu, Weihang Shi, and Wenwu Wang, “Automated fabric defect detection using multi-scale fusion MemAE,” The Visual Computer, pp. 1–15, 2024

    2024

  7. [15]

    Faster R-CNN: Towards real-time object detection with region proposal net- works,

    Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun, “Faster R-CNN: Towards real-time object detection with region proposal net- works,” IEEE transactions on pattern analysis and machine intelligence, vol. 39, no. 6, pp. 1137–1149, 2016

    2016

  8. [16]

    ultra- lytics/yolov5. github repository,

    G Jocher, A Stoken, J Borovec, A Chaurasia, and L Changyu, “ultra- lytics/yolov5. github repository,” YOLOv5, 2020

    2020

  9. [17]

    Research on Fabric Defect Detection Algorithm Based on Improved YOLOv8n Algorithm,

    Shunqi Mei, Yishan Shi, Heng Gao, and Li Tang, “Research on Fabric Defect Detection Algorithm Based on Improved YOLOv8n Algorithm,” Electronics, vol. 13, no. 11, pp. 2009, 2024

    2009

  10. [18]

    Fabric surface defect detection using SE-ssdnet,

    H. Q. Zhao and T. S. Zhang, “Fabric surface defect detection using SE-ssdnet,” Symmetry-Basel, vol. 14, no. 11, pp. 14, 2022

    2022

  11. [19]

    A novel multi-frequency coordinated module for sar ship detection,

    Chenchen Qiao, Fei Shen, Xuejun Wang, Ruixin Wang, Fang Cao, Sixian Zhao, and Chang Li, “A novel multi-frequency coordinated module for sar ship detection,” in 2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) . IEEE, 2022, pp. 804–811

    2022

  12. [20]

    A novel cross frequency-domain interaction learning for aerial oriented object detection,

    Weijie Weng, Weiming Lin, Feng Lin, Junchi Ren, and Fei Shen, “A novel cross frequency-domain interaction learning for aerial oriented object detection,” in Chinese Conference on Pattern Recognition and Computer Vision (PRCV) . Springer, 2023, pp. 292–305

    2023

  13. [21]

    PRC-light YOLO: An efficient lightweight model for fabric defect detection,

    Baobao Liu, Heying Wang, Zifan Cao, Yu Wang, Lu Tao, Jingjing Yang, and Kaibing Zhang, “PRC-light YOLO: An efficient lightweight model for fabric defect detection,” Applied Sciences , vol. 14, no. 2, pp. 21, 2024

    2024

  14. [22]

    Feature pyramid full granularity attention network for object detection in remote sensing imagery,

    Chang Liu, Xiao Qi, Hang Yin, Bowei Song, Ke Li, and Fei Shen, “Feature pyramid full granularity attention network for object detection in remote sensing imagery,” in International Conference on Intelligent Computing. Springer, 2024, pp. 332–353

    2024

  15. [23]

    Vision mamba: Efficient visual represen- tation learning with bidirectional state space model,

    Lianghui Zhu, Bencheng Liao, Qian Zhang, Xinlong Wang, Wenyu Liu, and Xinggang Wang, “Vision mamba: Efficient visual represen- tation learning with bidirectional state space model,” arXiv preprint arXiv:2401.09417, 2024

    2024 arXiv

  16. [24]

    Mambaad: Exploring state space models for multi-class unsu- pervised anomaly detection,

    Haoyang He, Yuhu Bai, Jiangning Zhang, Qingdong He, and Hongxu Chen, “Mambaad: Exploring state space models for multi-class unsu- pervised anomaly detection,” arXiv preprint arXiv:2404.06564 , 2024

    2024 arXiv

  17. [25]

    Vmamba: Visual state space model,

    Yue Liu, Yunjie Tian, Yuzhong Zhao, Hongtian Yu, Lingxi Xie, Yaowei Wang, Qixiang Ye, and Yunfan Liu, “Vmamba: Visual state space model,” arXiv preprint arXiv:2401.10166 , 2024

    2024 arXiv

  18. [26]

    A review on yolov8 and its advancements,

    Mupparaju Sohan, Thotakura Sai Ram, Rami Reddy, and Ch Venkata, “A review on yolov8 and its advancements,” in International Conference on Data Intelligence and Cognitive Informatics . 2024, pp. 529–545, Springer

    2024

  19. [27]

    Tph-yolov5: Improved yolov5 based on transformer prediction head for object detection on drone-captured scenarios,

    Xingkui Zhu, Shuchang Lyu, Xu Wang, and Qi Zhao, “Tph-yolov5: Improved yolov5 based on transformer prediction head for object detection on drone-captured scenarios,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 2778–2788

    2021

  20. [28]

    Eca-net: Efficient channel attention for deep convolutional neural networks,

    Qilong Wang, Banggu Wu, Pengfei Zhu, Peihua Li, Wangmeng Zuo, and Qinghua Hu, “Eca-net: Efficient channel attention for deep convolutional neural networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11534–11542

    2020

  21. [29]

    Attention-Based Multiscale Feature Fusion for Efficient Surface Defect Detection,

    Yuhao Zhao, Qing Liu, Hu Su, Jiabin Zhang, Hongxuan Ma, Wei Zou, and Song Liu, “Attention-Based Multiscale Feature Fusion for Efficient Surface Defect Detection,” Ieee Transactions on Instrumentation and Measurement, vol. 73, pp. 1–10, 2024

    2024

  22. [30]

    Fabric defect detection via a spatial cloze strategy,

    Zhengyang Lu, Yudian Zhang, Han Xu, and Han Chen, “Fabric defect detection via a spatial cloze strategy,” Textile Research Journal, vol. 93, no. 7-8, pp. 1612–1627, 2023

    2023

  23. [31]

    Tood: Task-aligned one-stage object detection,

    Chengjian Feng, Yujie Zhong, Yu Gao, Matthew R Scott, and Weilin Huang, “Tood: Task-aligned one-stage object detection,” in 2021 IEEE/CVF International Conference on Computer Vision (ICCV) . IEEE Computer Society, 2021, pp. 3490–3499

    2021

  24. [32]

    An anchor- free defect detector for complex background based on pixelwise adaptive multiscale feature fusion,

    Hantong Lu, Mengyuan Fang, Ying Qiu, and Weiqiang Xu, “An anchor- free defect detector for complex background based on pixelwise adaptive multiscale feature fusion,” Ieee Transactions on Instrumentation and Measurement, vol. 72, pp. 1–12, 2023

    2023

  25. [33]

    Fabric defect detection based on anchor-free network,

    Xianbao Wang, Weijie Fang, and Sheng Xiang, “Fabric defect detection based on anchor-free network,” Measurement Science and Technology , vol. 34, no. 12, pp. 14, 2023

    2023

  26. [34]

    Adaptively Fused Attention Module for the Fabric Defect Detection,

    Jin Wang, Jingru Yang, Guodong Lu, Cheng Zhang, Zhiyong Yu, and Ying Yang, “Adaptively Fused Attention Module for the Fabric Defect Detection,” Advanced Intelligent Systems , vol. 5, no. 2, pp. 10, 2023

    2023

  27. [35]

    Research on Tiny Target Detection Technology of Fabric Defects Based on Improved YOLO,

    X. Yue, Q. Wang, L. He, Y . X. Li, and D. Tang, “Research on Tiny Target Detection Technology of Fabric Defects Based on Improved YOLO,” Applied Sciences-Basel, vol. 12, no. 13, pp. 16, 2022

    2022

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Reviewed August 11, 2026 · model on record in the stance chip above.