Computer vision leveraging deep learning has achieved significant success in the last decade. Despite the promising performance of the existing deep vision inspection models, the extent of models’ reliability remains unknown. Structural health monitoring (SHM) is a crucial task for the safety and sustainability of structures, and thus, prediction mistakes can have fatal outcomes. In this paper, we use Bayesian inference for deep vision SHM models where uncertainty can be quantified using the Monte Carlo dropout sampling. Three independent case studies for cracks, local damage identification, and bridge component detection are investigated using Bayesian inference. Aside from better prediction results, the two uncertainty metrics, variations in softmax probability and entropy, are shown to have good correlations with misclassifications. However, modifying the decision or triggering human intervention can be challenging based on raw uncertainty outputs. Therefore, the concept of surrogate models is proposed to develop the models for uncertainty‐assisted segmentation and prediction quality tagging. The former refines the segmentation mask and the latter is used to trigger human interventions. The proposed framework can be applied to future deep vision SHM frameworks to incorporate model uncertainty in the inspection processes.

中文翻译：

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不确定性辅助的深视结构健康监测

利用深度学习的计算机视觉在过去十年中取得了巨大的成功。尽管现有的深度视觉检查模型具有令人鼓舞的性能，但是模型的可靠性范围仍然未知。结构健康监测（SHM）对于结构的安全性和可持续性至关重要，因此，预测错误可能会导致致命的后果。在本文中，我们将贝叶斯推论用于深度视觉SHM模型，其中可以使用蒙特卡罗辍学采样来量化不确定性。使用贝叶斯推理研究了三个独立的案例研究：裂纹，局部损伤识别和桥梁构件检测。除了更好的预测结果外，两个不确定度指标，softmax概率的变化和熵也显示与错误分类具有良好的相关性。然而，基于原始的不确定性输出，修改决策或触发人为干预可能具有挑战性。因此，提出了替代模型的概念来开发不确定性辅助分割和预测质量标记的模型。前者改进了分割蒙版，后者用于触发人为干预。提议的框架可以应用于未来的深度视觉SHM框架，以将模型不确定性纳入检查过程。