B-BACN: Bayesian Boundary-Aware Convolutional Network for Crack Characterization
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The accurate detection of crack boundaries is crucial for various purposes, such as condition monitoring, prognostics, and maintenance scheduling. To address this issue, we introduce a Bayesian Boundary-Aware Convolutional Network (B-BACN) that emphasizes the significance of both uncertainty quantification and boundary refinement to generate precise and reliable defect boundary detections. Our inspection model employs a multi-task learning approach, where we use Monte Carlo Dropout to learn the epistemic uncertainty and a Gaussian sampling function to predict each sample's aleatoric uncertainty. Moreover, we include a boundary refinement loss to B-BACN to enhance the determination of defect boundaries. The experimental results illustrate the effectiveness of our proposed approach in identifying crack boundaries with high accuracy, minimizing misclassification rate, and improving model calibration capabilities.
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