The industrial implementation of automated visual inspection leveraging deep learning is limited due to the labor-intensive labeling of datasets and the lack of datasets containing images of defects, which is especially the case in high-volume manufacturing with zero defect constraints. In this study, we present the FuseDecode Autoencoder (FuseDecode AE), a novel reconstruction-based anomaly detection model featuring incremental learning. Initially, the FuseDecode AE operates in an unsupervised manner on noisy data containing predominantly normal images and a small number of anomalous images. The predictions generated assist experts in distinguishing between normal and anomalous samples. Later, it adapts to weakly labeled datasets by retraining in a semi-supervised manner on normal data augmented with synthetic anomalies. As more real anomalous samples become available, the model further refines its capabilities through mixed-supervision learning on both normal and anomalous samples. Evaluation on a real industrial dataset of coating defects shows the effectiveness of the incremental learning approach. Furthermore, validation on the publicly accessible MVTec AD dataset demonstrates the FuseDecode AE's superiority over other state-of-the-art reconstruction-based models. These findings underscore its generalizability and effectiveness in automated visual inspection tasks, particularly in industrial settings.

中文翻译：

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用于工业视觉检测的新型 FuseDecode 自动编码器：逐步从无监督学习过渡到混合监督学习，减少人工操作，逐步改进异常检测


由于数据集的标记需要劳动密集型，并且缺乏包含缺陷图像的数据集，因此利用深度学习的自动视觉检测的工业实施受到限制，尤其是在零缺陷约束的大批量制造中。在这项研究中，我们提出了 FuseDecode 自动编码器 （FuseDecode AE），这是一种新颖的基于重建的异常检测模型，具有增量学习功能。最初，FuseDecode AE 以无监督的方式对包含主要正常图像和少量异常图像的噪声数据进行操作。生成的预测有助于专家区分正常样本和异常样本。后来，它通过对合成异常增强的正常数据以半监督方式进行再训练来适应弱标记的数据集。随着更多真实的异常样本可用，该模型通过对正常样本和异常样本进行混合监督学习来进一步完善其功能。对涂层缺陷的真实工业数据集的评估显示了增量学习方法的有效性。此外，对可公开访问的 MVTec AD 数据集的验证表明 FuseDecode AE 优于其他最先进的基于重建的模型。这些发现强调了它在自动视觉检测任务中的普遍性和有效性，尤其是在工业环境中。