目的 针对航天密封材料样本数量少且较难识别的问题,提出一种基于小样本深度学习的表面缺陷检测模型。方法 通过对航天密封圈样本图像的采集、预处理,制作了适用于无监督算法的数据集。在RegAD算法基础上,将残差网络ResNet与空间变换网络STN相结合,提出一种密封材料表面缺陷检测模型RegAD-RS。结果 在样本支撑集包含2个、4个和8个正常样本的情况下对模型进行训练,结果显示该模型能够准确识别材料表面缺陷,性能指标AUROC均在90%以上,最高可达97.29%。结论 基于小样本深度学习的航天密封材料表面缺陷检测模型展现了较强的检测优势,提高了航天密封特殊材料表面缺陷检测精度,能够有效保障航天精密工业品质量安全。
Abstract
The work aims to propose a surface defect detection model based on few-shot deep learning to address the problems of small sample size and difficulty in identification of aerospace sealing materials. By collecting and preprocessing the sample images of aerospace sealing rings, a dataset suitable for unsupervised algorithms was constructed. On the basis of the RegAD algorithm, a surface defect detection model for sealing materials, named RegAD-RS, was proposed by combining the Residual Network (ResNet) with the Spatial Transformer Network (STN). The model was trained under the conditions that the sample support set contained 2, 4 and 8 normal samples respectively. The results showed that the model could accurately identify the surface defects of materials, with the performance index AUROC (Area Under the Receiver Operating Characteristic Curve) all above 90% and the highest reaching 97.29%, demonstrating strong detection advantages. It improves the detection accuracy of surface defects of special aerospace sealing materials and can effectively ensure the quality and safety of aerospace precision industrial products.
关键词
密封材料 /
小样本 /
无监督学习 /
缺陷检测
Key words
sealing materials /
few-shot /
unsupervised learning /
defect detection
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