Quantitative assessment of cracks in concrete bridges is crucial for structural health monitoring and digital twin. However, the training of crack segmentation models relies heavily on annotation resources, and their segmentation capabilities are often unsatisfactory in terms of the accuracy of boundary location of thin cracks encountered in practice. In this paper, an active-learning-integrated crack segmentation transformer (ACS-Former) framework is proposed to maximize segmentation performance with limited annotation resources. The two-branch ACS-Former includes (1) a feature pyramid transformer (FPT) for multi-scale crack segmentation and (2) boundary difficulty-aware active learning (BDAL) to select informative images for labeling and incorporation into FPT training. Additionally, an adhesive climbing robot is proposed for image collection of hard-to-access components of large bridges. The on-site operational feasibility and practicability of the proposed ACS-Former and climbing robot are demonstrated by field experiments performed on in-service bridges, including the quantification of cracks narrower than 0.2 mm, as required by engineering codes.

中文翻译：

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使用主动学习集成变压器和无人机器人平台对混凝土结构的裂缝进行定量评估


混凝土桥梁裂缝的定量评估对于结构健康监测和数字孪生至关重要。然而，裂纹分割模型的训练在很大程度上依赖于标注资源，其分割能力在实际遇到的薄裂纹边界定位精度方面往往不尽如人意。在本文中，提出了一种主动学习集成的裂缝分割转换器 （ACS-Former） 框架，以在有限的注释资源下最大限度地提高分割性能。双分支 ACS-Former 包括 （1） 用于多尺度裂纹分割的特征金字塔变压器 （FPT） 和 （2） 边界困难感知主动学习 （BDAL），用于选择信息丰富的图像进行标记并纳入 FPT 训练。此外，还提出了一种粘性攀爬机器人，用于收集大型桥梁难以接近的组件的图像。在现役桥梁上进行的现场实验证明了拟议的 ACS-Former 和攀爬机器人的现场操作可行性和实用性，包括根据工程规范要求对窄于 0.2 毫米的裂缝进行量化。