Pyramidal anomaly detection with state-space models

Recent advancements in CNNs and transformer-based methods have significantly improved multiclass anomaly detection; however, accurately localizing small anomalies in industrial images remains challenging. Although promising, CNNs suffer in capturing long-range dependencies and the effectiveness of the transformers is hindered by their quadratic computational costs. This paper proposes a novel State Space Model (SSMs)-based multi-class anomaly detection method, termed Pyramidal Anomaly Detection with State-Space Models (PAD-SSM), which offers state-of-the-art performance with linear complexity. The main contribution of our work is the Pyramidal Scanning Approach (PSA), which performs the scanning hierarchically. This enables more localized and scale-aware analysis. PSA operates in a pyramid-like style, recursively partitions the image into equal non-overlapping patches across multiple scales. The SSM is then applied independently to each patch. This approach enables detailed, multi-scale, and high-resolution analysis, which is crucial for detecting small anomalies in industrial images. We integrate the PSA block with a pre-trained encoder for multi-scale feature extraction, a feature adapter to transform the encoded features to a target domain, and a synthetic anomaly generator that introduces noise at the feature level, further enhancing detection capabilities. Rigorous evaluations on the MVTec-AD and VisA datasets demonstrate that our method achieves state-of-the-art mean AU-ROC of 98.7, AP of 99.6, and F1max of 98.1 on MVTec-AD benchmark while using the lowest number of FLOPS (e.g., 10.3% fewer FLOPs than the next best method).