目的 提升质检过程中新材料地板的表面缺陷检测精度。方法 通过翻转、水平迁移和垂直迁移对采集到的缺陷图像进行扩充，构建新材料地板缺陷数据集。基于YOLOv5算法，增加一个预测头，使算法对微小缺陷更加敏感;其次在网络的特征融合层应用Swin Transformer模块，形成注意力机制预测头，提高网络特征提取效率;然后在网络主干末端加入SE模块，使网络提取有用的信息特征，提高模型精度。结果 实验结果表明，提出的新材料地板表面缺陷检测方法能够准确判别地板好坏，并能够识别出白色杂质、黑斑、边损、气泡胶等4类表面缺陷，各缺陷类型的平均精确均值为82.30%，比YOLOv5 Baseline提高了6.58%，相比其他典型目标检测算法也能够更准确和快速地识别地板表面缺陷。结论 通过改进的YOLOv5算法可以更准确地对地板表面缺陷进行分类与定位，从而大大提高工业质检效率。

The work aims to improve the surface defect detection accuracy of new material floor during quality inspection. The defect images collected were expanded by flipping, horizontal migration and vertical migration, and the defect data set of new material floor was constructed. Based on YOLOv5, a prediction head was added to make the algorithm more sensitive to tiny defects. Next, the Swin Transformer module was applied in the feature fusion layer of the network to form the attentional mechanism prediction head and improve the efficiency of network feature extraction. Then, the SE module was added at the end of the backbone to enable the network to extract useful feature information and improve the model accuracy. The experimental results showed that the proposed method could accurately distinguish the quality of floor and identify four kinds of surface defects including white impurity, black spot, edge damage and bubble gum. The mean average precision for each defect type was 82.30%, which was 6.58% higher than that of YOLOv5 Baseline. Compared with other typical target detection algorithms, it could identify floor surface defects more accurately and quickly. The improved YOLOv5 algorithm can classify and locate the surface defects of the floor more accurately, thus greatly improving the efficiency of industrial quality inspection.