用流模型+局部特征提升压铸件缺陷检测精度
PatchFlow: Leveraging a Flow-Based Model with Patch Features
- 结合局部邻域感知补丁特征与归一化流模型
- 在MVTec AD上错误率降20%，图像AUROC达99.28%
- 无需异常样本即可在自研数据集达95.77%准确率
压铸因其高精度和光滑表面，在多个工业领域至关重要，但表面缺陷仍是质量控制的主要障碍。近年来，计算机视觉技术被用于自动化改进缺陷检测。本文将局部邻域感知的补丁特征与归一化流模型结合，并引入适配器模块，弥合通用预训练特征提取器与工业产品图像之间的差距，提升了自动异常检测的效率与准确性。相比现有最优方法，本方法在MVTec AD数据集上错误率降低20%，图像级AUROC达到99.28%；在VisA数据集上，图像级AUROC为96.48%，错误率减少28.2%。此外，在自有压铸数据集上的实验表明，无需异常样本训练，异常检测准确率达95.77%。结果展示了利用计算机视觉与深度学习技术提升压铸行业检测能力的潜力。
原文摘要 · Abstract (English)
Die casting plays a crucial role across various industries due to its ability to craft intricate shapes with high precision and smooth surfaces. However, surface defects remain a major issue that impedes die casting quality control. Recently, computer vision techniques have been explored to automate and improve defect detection. In this work, we combine local neighbor-aware patch features with a normalizing flow model and bridge the gap between the generic pretrained feature extractor and industrial product images by introducing an adapter module to increase the efficiency and accuracy of automated anomaly detection. Compared to state-of-the-art methods, our approach reduces the error rate by 20\% on the MVTec AD dataset, achieving an image-level AUROC of 99.28\%. Our approach has also enhanced performance on the VisA dataset , achieving an image-level AUROC of 96.48\%. Compared to the state-of-the-art models, this represents a 28.2\% reduction in error. Additionally, experiments on a proprietary die casting dataset yield an accuracy of 95.77\% for anomaly detection, without requiring any anomalous samples for training. Our method illustrates the potential of leveraging computer vision and deep learning techniques to advance inspection capabilities for the die casting industry
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