Reliable fabric defect detection via Bayesian uncertainty modeling

Despite the demonstrated capability of deep learning models in detecting anomalies in textile images, their predictions in real-world applications tend to be overly confident, especially when faced with defect types not previously encountered in the training set or when dealing with low-quality annotations. This excessive confidence in predictions limits the practical application of deep learning methods in textile defect detection, as it fails to provide inspectors with reliable guidance on when to trust the model's predictions and when manual verification is necessary. To address this issue, this paper introduces a Bayesian fabric anomaly detection model that utilizes Variational Inference (VI) to apply Bayesian inference to the widely used U-Net architecture. During the inference phase, the model employs Monte Carlo sampling to perform multiple forward passes, generating three types of uncertainty estimations and per-pixel uncertainty maps, thus providing comprehensive evidence for decision-making. This method not only estimates the uncertainty of model predictions but also improves the F1 score by 2-4% over the baseline U-Net model in the frequency domain. This study proves the Bayesian approach boosts fabric anomaly detection and decision-making by optimizing model performance and reducing reliance on inaccurate predictions.