  Accurately detecting crack boundaries is crucial for reliability assessment
and risk management of structures and materials, such as structural health
monitoring, diagnostics, prognostics, and maintenance scheduling. Uncertainty
quantification of crack detection is challenging due to various stochastic
factors, such as measurement noises, signal processing, and model
simplifications. A machine learning-based approach is proposed to quantify both
epistemic and aleatoric uncertainties concurrently. We introduce a Bayesian
Boundary-Aware Convolutional Network (B-BACN) that emphasizes uncertainty-aware
boundary refinement to generate precise and reliable crack boundary detections.
The proposed method 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 proposed method is demonstrated with benchmark experimental
results and compared with several existing methods. The experimental results
illustrate the effectiveness of our proposed approach in uncertainty-aware
crack boundary detection, minimizing misclassification rate, and improving
model calibration capabilities.
