Explainable Deep Convolutional Multi-Type Anomaly Detection

• URL: http://arxiv.org/abs/2511.11165v1
• Date: Fri, 14 Nov 2025 11:04:34 GMT
• Title: Explainable Deep Convolutional Multi-Type Anomaly Detection
• Authors: Alex George, Lyudmila Mihaylova, Sean Anderson,
• Abstract summary: Most explainable anomaly detection methods often identify anomalies but lack the capability to differentiate the type of anomaly.<n>We propose MultiTypeFCDD, a simple and lightweight convolutional framework designed as a practical alternative for explainable multi-type anomaly detection.<n>We evaluate our proposed method on the Real-IAD dataset and it delivers results competitive with state-of-the-art complex models.
• Score: 0.3845204730009788
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most explainable anomaly detection methods often identify anomalies but lack the capability to differentiate the type of anomaly. Furthermore, they often require the costly training and maintenance of separate models for each object category. The lack of specificity is a significant research gap, as identifying the type of anomaly (e.g., "Crack" vs. "Scratch") is crucial for accurate diagnosis that facilitates cost-saving operational decisions across diverse application domains. While some recent large-scale Vision-Language Models (VLMs) have begun to address this, they are computationally intensive and memory-heavy, restricting their use in real-time or embedded systems. We propose MultiTypeFCDD, a simple and lightweight convolutional framework designed as a practical alternative for explainable multi-type anomaly detection. MultiTypeFCDD uses only image-level labels to learn and produce multi-channel heatmaps, where each channel is trained to correspond to a specific anomaly type. The model functions as a single, unified framework capable of differentiating anomaly types across multiple object categories, eliminating the need to train and manage separate models for each object category. We evaluated our proposed method on the Real-IAD dataset and it delivers results competitive with state-of-the-art complex models at significantly reduced parametric load and inference times. This makes it a highly practical and viable solution for real-world applications where computational resources are tightly constrained.

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