Abstract
Automating visual inspection in industrial production lines is essential for increasing product quality across various industries. Anomaly detection (AD) methods serve as robust tools for this purpose. However, existing public datasets primarily consist of images without anomalies, limiting the practical application of AD methods in production settings. To address this challenge, we present (1) the Valeo Anomaly Dataset (VAD), a novel real-world industrial dataset comprising 5000 images, including 2000 instances of challenging real defects across more than 20 subclasses. Acknowledging that traditional AD methods struggle with this dataset, we introduce (2) Segmentation-based Anomaly Detector (SegAD). First, SegAD leverages anomaly maps as well as segmentation maps to compute local statistics. Next, SegAD uses these statistics and an optional supervised classifier score as input features for a Boosted Random Forest (BRF) classifier, yielding the final anomaly score. Our SegAD achieves state-of-the-art performance on both VAD (+2.1% AUROC) and the VisA dataset (+0.4% AUROC). The code and the models are publicly available.
Abstract (translated)
在工业生产线上自动化视觉检查对于提高产品质量至关重要，异常检测（AD）方法作为这一目的的有力工具显得至关重要。然而，现有的公共数据集主要包含没有异常的图像，这限制了AD方法在生产环境中的实际应用。为解决这个问题，我们提出了（1）Valeo异常数据集（VAD），这是一个由5000个图像组成的新兴工业现实数据集，包括2000个具有超过20个亚类的具有挑战性的真实缺陷实例。承认传统AD方法在这个数据集上挣扎，我们引入了（2）基于分段的异常检测器（SegAD）。首先，SegAD利用异常图和分割图计算局部统计。接下来，SegAD将这些统计量作为输入特征输入到Boosted Random Forest（BRF）分类器中，产生最终的异常得分。我们的SegAD在VAD (+2.1% AUROC)和VisA数据集 (+0.4% AUROC)上实现了最先进的性能。代码和模型公开可用。
URL
https://arxiv.org/abs/2405.04953
