Ablation experiments of different modules.

Accurate detection of fabric defects is crucial for quality control in the textile industry. However, the task of fabric defect detection remains highly challenging due to the complex textures and diverse defect patterns. To address the issues of inaccurate localization and false positives caused by complex textures and varying defect sizes, this paper proposes an improved YOLOv8-based fabric defect detection method. First, Dynamic Snake Convolution is introduced into the backbone network to enhance sensitivity to elongated and subtle defects, improving the extraction of edge and texture details. Second, a Channel Priority Convolutional Attention mechanism is incorporated after the Spatial Pyramid Pooling layer to enable more precise defect localization by leveraging multi-scale structures and channel priors. Finally, the feature fusion network integrates Partial Convolution and Efficient Multi-scale Attention, optimizing the fusion of information across different feature levels and spatial scales, which enhances the richness and accuracy of feature representations while reducing computational complexity. Experimental results demonstrate a significant improvement in detection performance. Specifically, mAP@0.5 increased by 2.9%, precision improved by 3.5%, and mAP@0.5:0.95 rose by 2.3%, highlighting the model’s superior capability in detecting complex defects. The project is available at https://github.com/lilian998/fabric.

织物缺陷的精准检测对于纺织行业的质量管控至关重要。然而，受复杂织物纹理与多样缺陷形态的影响，织物缺陷检测任务依旧面临极大挑战。针对复杂纹理与缺陷尺寸不一导致的定位不准及误检问题，本文提出了一种基于改进YOLOv8的织物缺陷检测方法。首先，将动态蛇形卷积（Dynamic Snake Convolution）引入主干网络，以提升对细长型及细微缺陷的感知能力，强化边缘与纹理细节的提取效果。其次，在空间金字塔池化（Spatial Pyramid Pooling）层后加入通道优先卷积注意力机制，借助多尺度结构与通道先验信息实现更精准的缺陷定位。最后，特征融合网络整合了部分卷积与高效多尺度注意力机制，优化了不同特征层级与空间尺度间的信息融合过程，在提升特征表征丰富度与准确度的同时降低了计算复杂度。实验结果表明，该方法的检测性能得到了显著提升。具体而言，其mAP@0.5提升了2.9%，精确率提升了3.5%，mAP@0.5:0.95提升了2.3%，凸显了该模型在复杂缺陷检测上的优异性能。本项目开源地址为：https://github.com/lilian998/fabric。