In the realm of industrial applications, detecting surface anomalies in manufacturing processes is challenging. Various methodologies have emerged, utilizing pre-trained networks for feature extraction and processing through memory banks, normalizing flow, and knowledge distillation. The paper introduces a new method based on target-specific embedding using contrastive training with artificially generated defective samples. By exploiting intrinsic surface properties, the approach derives meaningful representations from defect-free samples to calculate anomaly scores effectively. Experiments on datasets demonstrate the competitiveness of this approach compared to existing methods.
The unsupervised anomaly detection domain has gained considerable attention in industrial applications due to the efficacy of Convolutional Neural Networks (CNNs) in analyzing visual data like images and surfaces. Deep learning enables intricate representations from extensive datasets, enhancing accuracy in quality control anomaly detection.
Approaches based on pre-trained features have seen a surge, offering impressive results while minimizing inference time. This paper proposes a new method broadening approaches for anomaly detection by emphasizing optimal "anomaly detection" capabilities for target textures.
Feature extraction from pre-trained models aims to compile representative features emphasizing differences in the presence of anomalies. The proposed method employs a defect generation technique during training to assist in extracting features responsive to defects.
Contrastive training poses challenges for surfaces as minor defects can remain similar to defect-free textures. To address this issue, deep features extracted from pre-trained models are utilized due to their substantial receptive field and low resolution.
The methodology includes a contrastive cosine loss function to amplify differences between defective and non-defective samples' embeddings. A decoder is introduced to prevent trivial representation by reconstructing embedded features back to their original dimensions.
Anomaly scoring involves k-means clustering on defect-free training dataset embeddings for efficient computation of anomaly scores during testing. The proposed approach showcases state-of-the-art performance in surface defect detection across different datasets.
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by Simon Thomin... at arxiv.org 03-05-2024
https://arxiv.org/pdf/2403.01859.pdfDeeper Inquiries