InstAD: Instance-aware Segmentation Framework for Zero-shot Multi-instance Anomaly Detection

Multi-instance anomaly detection and segmentation play a crucial role in automated industrial inspection. Previous works mainly focus on single-instance detection tasks that require well-aligned input and extensive training sets. In this work, we introduce InstAD, a zero-shot multi-instance anomaly detection framework that achieves high accuracy with unaligned multi-instances. Combining segmentation results from the Segment Anything Model [1] and Grounded-SAM [2], we further refine the segmentation results with the proposed Adaptive Bandwidth Segmentation Refinement scheme to achieve accurate multi-instance segmentation. By using our instance-aware anomaly detection strategy, we achieve 93.0% and 99.1% for image-level and pixel-level AUROC, respectively, with the four-shot setting on the multi-instance classes of the VisA and MPDD datasets. Under the zero-shot scenario, we reach 87.2% and 98.4% for image-level and pixel-level AUROC, respectively. Our experiments on public datasets show that the proposed InstAD method significantly outperforms SOTA methods for multi-instance anomaly detection and segmentation.