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<p>Textile defect detection remains challenging because repetitive fabric textures often dominate feature responses, making subtle, elongated, and small-scale defects difficult to distinguish from complex backgrounds. To address this problem, this paper proposes DTM-YOLO, a texture-regularity-aware detection framework based on YOLOv11. Its core component is a Texture Suppression Attention Module, which models local statistical deviations from periodic texture patterns and performs residual-guided channel recalibration to enhance defect-sensitive responses while suppressing texture-dominant background features. Dynamic Snake Convolution is embedded into C3k2 blocks to improve continuity modeling of elongated defects, and a multi-scale assisted feature fusion neck is adapted to preserve shallow spatial details for small defect detection. The proposed framework is evaluated on two datasets: a self-constructed textile defect dataset and the public Tianchi fabric defect dataset. On the self-constructed dataset, DTM-YOLO improves mAP@0.5, mAP@0.5:0.95, and precision by 4.9%, 4.02%, and 5.7%, respectively, compared with YOLOv11n. Generalization results on the Tianchi dataset further indicate improved robustness under external textile data.</p>

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DTM-YOLO: a texture-regularity-aware YOLO framework for textile defect detection under complex backgrounds

  • 期刊论文

  • 发表日期：

    2026年8月3日

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  • Yuheng Pan,
  • Chao Fan,
  • Weijia Lu,
  • Jia Cong,
  • Guoyan Li

摘要

Textile defect detection remains challenging because repetitive fabric textures often dominate feature responses, making subtle, elongated, and small-scale defects difficult to distinguish from complex backgrounds. To address this problem, this paper proposes DTM-YOLO, a texture-regularity-aware detection framework based on YOLOv11. Its core component is a Texture Suppression Attention Module, which models local statistical deviations from periodic texture patterns and performs residual-guided channel recalibration to enhance defect-sensitive responses while suppressing texture-dominant background features. Dynamic Snake Convolution is embedded into C3k2 blocks to improve continuity modeling of elongated defects, and a multi-scale assisted feature fusion neck is adapted to preserve shallow spatial details for small defect detection. The proposed framework is evaluated on two datasets: a self-constructed textile defect dataset and the public Tianchi fabric defect dataset. On the self-constructed dataset, DTM-YOLO improves mAP@0.5, mAP@0.5:0.95, and precision by 4.9%, 4.02%, and 5.7%, respectively, compared with YOLOv11n. Generalization results on the Tianchi dataset further indicate improved robustness under external textile data.