Detecting accurate crack boundaries is important for condition monitoring, prognostics, and maintenance scheduling. In this work, we propose a Bayesian Boundary-Aware Convolutional Network (B-BACN) to tackle this problem, that emphasizes the importance of both uncertainty quantification and boundary refinement for producing accurate and trustworthy detections of defect boundaries. We formulate the inspection model using multi-task learning. The epistemic uncertainty is learned using Monte Carlo Dropout, and the model also learns to predict each samples aleatoric uncertainty. A boundary refinement loss is added to improve the determination of defect boundaries. Experimental results demonstrate the effectiveness of the proposed method in accurately identifying crack boundaries, reducing misclassification and enhancing model calibration.

翻译：精确检测裂纹边界对于状态监测、预测性维护及维护调度至关重要。本文提出了一种贝叶斯边界感知卷积网络（B-BACN）来解决这一问题，该网络强调不确定性量化与边界细化在实现缺陷边界的准确可信检测中的重要性。我们采用多任务学习构建检测模型。通过蒙特卡洛丢弃法学习认知不确定性，同时模型也学习预测每个样本的偶然不确定性。引入边界细化损失以改进缺陷边界的确定。实验结果表明，所提方法在精确识别裂纹边界、降低误分类率及增强模型校准方面具有有效性。