The methods for unsupervised visual inspection use algorithms that are developed, trained and evaluated on publicly available datasets. However, these datasets do not reflect genuine industrial conditions, and thus current methods are not evaluated in real-world industrial production contexts. To answer this shortcoming, we introduce AutoVI, an industrial dataset of visual defects that can be encountered on automotive assembly lines. This dataset, comprising six inspection tasks, was designed as a benchmark to assess the performance of defect detection methods under realistic acquisition conditions. We analyze the performance of current state-of-the-art methods and discuss the difficulties specifically encountered in the industrial context. Our results show that current methods leave considerable room for improvement. We make AutoVI publicly available to develop unsupervised detection methods that will be better suited to real industrial tasks.

中文翻译：

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检测工业环境中的视觉异常：在 AutoVI 数据集上测试无监督方法


无监督视觉检查方法使用在公开数据集上开发、训练和评估的算法。然而，这些数据集并不能反映真实的工业条件，因此当前的方法没有在现实世界的工业生产环境中进行评估。为了解决这个缺点，我们引入了 AutoVI，这是一个汽车装配线上可能遇到的视觉缺陷的工业数据集。该数据集包含六个检查任务，旨在作为评估实际采集条件下缺陷检测方法性能的基准。我们分析了当前最先进方法的性能，并讨论了在工业背景下具体遇到的困难。我们的结果表明，当前的方法还有很大的改进空间。我们公开 AutoVI，以开发更适合实际工业任务的无监督检测方法。