提升布料缺陷检测精度，专攻条纹类缺陷与小目标识别。
SPFFNet: Strip Perception and Feature Fusion Spatial Pyramid Pooling for Fabric Defect Detection
- 设计条纹感知模块与注意力增强的空间金字塔，强化多尺度特征提取。
- 在Tianchi和自建数据集上mAP提升0.8%-13.2%，显著优于现有方法。
- 适合工业质检场景，尤其对细长缺陷和类别不平衡问题敏感的场景。
布料缺陷检测对质量控制至关重要，但现有方法常受复杂背景和形状特异性缺陷影响。本文基于YOLOv11提出改进模型：引入条纹感知模块（SPM）通过多尺度卷积提升特征捕获能力；将空间金字塔池化快速版（SPPF）融合挤压-激励机制，形成SE-SPPF模块，更有效整合空间与通道信息；提出新型焦点增强完整交并比（FECIoU）损失，通过焦点损失自适应调整难检样本权重，缓解尺度差异与类别不平衡问题。实验表明，本模型在Tianchi数据集上mAP提升0.8%-8.1%，在自建数据集上提升1.6%-13.2%，优于其他先进方法。
原文摘要 · Abstract (English)
Defect detection in fabrics is critical for quality control, yet existing methods often struggle with complex backgrounds and shape-specific defects. In this paper, we propose an improved fabric defect detection model based on YOLOv11. To enhance the detection of strip defects, we introduce a Strip Perception Module (SPM) that improves feature capture through multi-scale convolution. We further enhance the spatial pyramid pooling fast (SPPF) by integrating a squeeze-and-excitation mechanism, resulting in the SE-SPPF module, which better integrates spatial and channel information for more effective defect feature extraction. Additionally, we propose a novel focal enhanced complete intersection over union (FECIoU) metric with adaptive weights, addressing scale differences and class imbalance by adjusting the weights of hard-to-detect instances through focal loss. Experimental results demonstrate that our model achieves a 0.8-8.1% improvement in mean average precision (mAP) on the Tianchi dataset and a 1.6-13.2% improvement on our custom dataset, outperforming other state-of-the-art methods.
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