Experimental environment configuratio.

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.

精准检测织物瑕疵对于纺织行业的质量管控至关重要。然而，受限于织物复杂纹理与多样瑕疵样式，织物瑕疵检测任务仍极具挑战性。针对复杂纹理及瑕疵尺寸不一引发的定位偏差与误检问题，本文提出一种基于改进YOLOv8的织物瑕疵检测方法。
首先，将动态蛇形卷积（Dynamic Snake Convolution）引入主干网络，以增强对细长型细微瑕疵的敏感度，提升边缘与纹理细节的提取效果。其次，在空间金字塔池化层后接入通道优先卷积注意力机制，依托多尺度结构与通道先验实现更精准的瑕疵定位。最后，特征融合网络融合部分卷积与高效多尺度注意力模块，优化不同特征层级与空间尺度间的信息交互融合，在提升特征表征丰富度与准确性的同时降低计算复杂度。
实验结果表明，该模型的检测性能获得显著提升：具体而言，mAP@0.5提升2.9%，精确率提升3.5%，mAP@0.5:0.95提升2.3%，凸显了该模型在复杂瑕疵检测场景下的优异性能。本项目开源地址为https://github.com/lilian998/fabric。