Deep Autoencoders for Anomaly Detection in Textured Images using CW-SSIM

• URL: http://arxiv.org/abs/2208.14045v1
• Date: Tue, 30 Aug 2022 08:01:25 GMT
• Title: Deep Autoencoders for Anomaly Detection in Textured Images using CW-SSIM
• Authors: Andrea Bionda, Luca Frittoli, Giacomo Boracchi
• Abstract summary: We show that adopting a loss function based on Complex Wavelet Structural Similarity (CW-SSIM) yields superior detection performance on this type of images. 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.
• Score: 5.042611743157464
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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