Deep Autoencoders for Anomaly Detection in Textured Images using CW-SSIM
This addresses industrial monitoring problems, such as defect detection in tissues, but is incremental as it modifies the loss function in an existing method.
The paper tackled anomaly detection in textured images by training a deep autoencoder with a CW-SSIM loss function, achieving comparable or superior performance to state-of-the-art methods on benchmarks.
Detecting anomalous regions in images is a frequently encountered problem in industrial monitoring. A relevant example is the analysis of tissues and other products that in normal conditions conform to a specific texture, while defects introduce changes in the normal pattern. We address the anomaly detection problem by training a deep autoencoder, and we show that adopting a loss function based on Complex Wavelet Structural Similarity (CW-SSIM) yields superior detection performance on this type of images compared to traditional autoencoder loss functions. Our experiments on well-known anomaly detection benchmarks show that a simple model trained with this loss function can achieve comparable or superior performance to state-of-the-art methods leveraging deeper, larger and more computationally demanding neural networks.