复杂工业图像的有监督异常检测
Supervised Anomaly Detection for Complex Industrial Images
AI总结：
针对现有公开异常检测数据集多无异常图像的问题，提出含真实缺陷的VAD数据集及基于分割的SegAD检测器，在两个数据集上取得SOTA性能，代码模型已开源。
摘要：
在工业生产线中实现视觉检测自动化对提升各行业产品质量至关重要。异常检测（AD）方法是实现这一目标的有力工具。然而，现有公开数据集主要由无异常图像构成，限制了AD方法在生产场景中的实际应用。为应对这一挑战，我们提出：（1）Valeo Anomaly Dataset（VAD，法雷奥异常数据集），这是一个全新的真实工业数据集，包含5000张图像，其中涵盖20多个子类的2000个具有挑战性的真实缺陷实例。鉴于传统AD方法在该数据集上表现不佳，我们提出（2）Segmentation-based Anomaly Detector（SegAD，基于分割的异常检测器）。首先，SegAD利用异常图和分割图计算局部统计量。接着，SegAD将这些统计量和可选的有监督分类器分数作为输入特征，输入Boosted Random Forest（BRF，提升随机森林）分类器，得到最终异常分数。我们的SegAD在VAD（AUROC提升2.1%）和VisA数据集（AUROC提升0.4%）上均取得了当前最优性能。代码和模型已公开可用。
英文摘要：
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.
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