Conventional anomaly detection techniques based on reconstruction via denoising diffusion model are widely used due to their ability to identify anomaly locations and shapes with high performance. However, there is a limitation in determining appropriate noise parameters that can degrade anomalies while preserving normal characteristics. Also, due to the volatility of the diffusion model, normal regions can fluctuate considerably during reconstruction, resulting in false detection. In this paper, we propose a method to detect anomalies by analysis of reconstruction trend depending on the degree of degradation, effectively solving the both problems of existing methods. The proposed method is validated on an open dataset for industrial anomaly detection, improving the performance of existing methods on a number of evaluation criteria. With the ease of combination with existing anomaly detection methods, it provides a tradeoff between computational cost and performance, allowing it high application potential in manufacturing industry.

通过分析降解程度引起的重构趋势，我们提出了一种检测异常的方法，有效解决了现有方法中存在的两个问题，同时在工业异常检测的开放数据集上验证了该方法的性能，与现有异常检测方法的结合便利性为制造业提供了计算成本和性能之间的权衡，具有高应用潜力。

无监督异常检测基于扩散趋势分析