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

Bionda, Andrea; Frittoli, Luca; Boracchi, Giacomo

doi:10.1007/978-3-031-06430-2_56

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…Hu et al [41] introduced a surface defect inspection method based on a reconstruction network using a combined structural and L1 loss. A deep autoencoder using CW-SSIM for detecting anomalous regions in textured images was proposed in [26]. Despite improvements, these methods still face challenges.…”

Section: Related Workmentioning

confidence: 99%

“…An unsupervised reconstruction-based method for surface defect detection using a combined structural similarity and mean absolute (L1) loss was proposed in [25]. Bionda et al [26] proposed a deep autoencoder for anomaly detection based on Complex Wavelet Structural Similarity (CW-SSIM). Chamberland et al [27] proposed a method to detect defects on cast components using a convolution neural network (CNN) autoencoder.…”

Section: Introductionmentioning

confidence: 99%

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Autoencoder-Based Unsupervised Surface Defect Detection Using Two-Stage Training

Getachew Shiferaw,

Yao

2024

J. Imaging

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Accurately detecting defects while reconstructing a high-quality normal background in surface defect detection using unsupervised methods remains a significant challenge. This study proposes an unsupervised method that effectively addresses this challenge by achieving both accurate defect detection and a high-quality normal background reconstruction without noise. We propose an adaptive weighted structural similarity (AW-SSIM) loss for focused feature learning. AW-SSIM improves structural similarity (SSIM) loss by assigning different weights to its sub-functions of luminance, contrast, and structure based on their relative importance for a specific training sample. Moreover, it dynamically adjusts the Gaussian window’s standard deviation (σ) during loss calculation to balance noise reduction and detail preservation. An artificial defect generation algorithm (ADGA) is proposed to generate an artificial defect closely resembling real ones. We use a two-stage training strategy. In the first stage, the model trains only on normal samples using AW-SSIM loss, allowing it to learn robust representations of normal features. In the second stage of training, the weights obtained from the first stage are used to train the model on both normal and artificially defective training samples. Additionally, the second stage employs a combined learned Perceptual Image Patch Similarity (LPIPS) and AW-SSIM loss. The combined loss helps the model in achieving high-quality normal background reconstruction while maintaining accurate defect detection. Extensive experimental results demonstrate that our proposed method achieves a state-of-the-art defect detection accuracy. The proposed method achieved an average area under the receiver operating characteristic curve (AuROC) of 97.69% on six samples from the MVTec anomaly detection dataset.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Autoencoder-Based Unsupervised Surface Defect Detection Using Two-Stage Training

Getachew Shiferaw,

Yao

2024

J. Imaging

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“…Anomaly detection and failure prediction are two related fields applied to diverse data, including time series [28][29][30][31][32] and images [33][34][35]. This research focuses on failure prediction applied to multivariate time series data acquired by multiple IoT sensors connected to an industrial machine.…”

Section: Related Workmentioning

confidence: 99%

Predicting Machine Failures from Multivariate Time Series: An Industrial Case Study

Pinciroli Vago,

Forbicini,

Fraternali

2024

Machines

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Non-neural machine learning (ML) and deep learning (DL) are used to predict system failures in industrial maintenance. However, only a few studies have assessed the effect of varying the amount of past data used to make a prediction and the extension in the future of the forecast. This study evaluates the impact of the size of the reading window and of the prediction window on the performances of models trained to forecast failures in three datasets of (1) an industrial wrapping machine working in discrete sessions, (2) an industrial blood refrigerator working continuously, and (3) a nitrogen generator working continuously. A binary classification task assigns the positive label to the prediction window based on the probability of a failure to occur in such an interval. Six algorithms (logistic regression, random forest, support vector machine, LSTM, ConvLSTM, and Transformers) are compared on multivariate time series. The dimension of the prediction windows plays a crucial role and the results highlight the effectiveness of DL approaches in classifying data with diverse time-dependent patterns preceding a failure and the effectiveness of ML approaches in classifying similar and repetitive patterns preceding a failure.

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AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection

Cao,

Zhang,

Frittoli

et al. 2024

Lecture Notes in Computer Science

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

Cited by 5 publications

References 24 publications

Autoencoder-Based Unsupervised Surface Defect Detection Using Two-Stage Training

Autoencoder-Based Unsupervised Surface Defect Detection Using Two-Stage Training

Predicting Machine Failures from Multivariate Time Series: An Industrial Case Study

AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection

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