无需训练，用DINOv2提取特征实现复杂工业缺陷的精准检测与分割。
SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect
- 基于DINOv2特征提取，仅用少量正常样本构建记忆库。
- 在MVTec AD 2数据集上两个测试集均达竞争力表现。
- 适合真实工业场景中光照多变、缺陷多样等复杂情况。
本文提出针对CVPR 2025 VAND 3.0挑战赛第1赛道「Adapt & Detect」的解决方案：在真实工业应用中实现鲁棒异常检测。真实工业场景中异常常具有物理复杂性，如透明/反光表面、遮挡和低对比度污染。近期发布的MVTec AD 2数据集显著缩小了公开基准与真实异常之间的差距。为应对该数据集带来的挑战——包括复杂多变的光照条件及尺度差异大的真实异常——我们提出一种完全无需训练的异常检测与分割方法SuperAD。该方法利用DINOv2模型强大的表征能力，仅选取少量正常参考图像，构建记忆库；通过测试图像特征与记忆库间的最近邻匹配实现异常分割。SuperAD在MVTec AD 2数据集的两个测试集上均取得有竞争力的结果。
原文摘要 · Abstract (English)
In this technical report, we present our solution to the CVPR 2025 Visual Anomaly and Novelty Detection (VAND) 3.0 Workshop Challenge Track 1: Adapt & Detect: Robust Anomaly Detection in Real-World Applications. In real-world industrial anomaly detection, it is crucial to accurately identify anomalies with physical complexity, such as transparent or reflective surfaces, occlusions, and low-contrast contaminations. The recently proposed MVTec AD 2 dataset significantly narrows the gap between publicly available benchmarks and anomalies found in real-world industrial environments. To address the challenges posed by this dataset--such as complex and varying lighting conditions and real anomalies with large scale differences--we propose a fully training-free anomaly detection and segmentation method based on feature extraction using the DINOv2 model named SuperAD. Our method carefully selects a small number of normal reference images and constructs a memory bank by leveraging the strong representational power of DINOv2. Anomalies are then segmented by performing nearest neighbor matching between test image features and the memory bank. Our method achieves competitive results on both test sets of the MVTec AD 2 dataset.
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