In bridge health monitoring (BHM), crack identification and width measurement are two of the most important indices for evaluating the functionality of bridges. In order to reduce the labor cost in field detection, researchers have proposed a variety of deep learning (DL)-based detection techniques for crack recognition. However, some problems still exist in extending these techniques to practical applications, such as data annotation difficulty, limited model generalization ability, and inaccuracy of the DL identification of the actual crack width measurement. In this paper, an application-oriented multistage crack recognition framework is proposed, namely, Convolutional Active Learning Identification-Segmentation-Measurement (CAL-ISM). It includes four steps: (1) pretraining of the benchmark classification model, (2) retraining of the semisupervised active learning model, (3) pixel-level crack segmentation, and (4) crack width measurement. Beyond numerical experiments, the performance of the CAL-ISM is validated for practical applications: (i) bridge column test specimen and (ii) field BHM projects. In conclusion, the obtained results from these applications shed light on the high potential of CAL-ISM for BHM applications, which is recommended in future deployments for BHM.

中文翻译：

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用于桥梁裂缝识别、分割和测量的多阶段半监督主动学习框架

在桥梁健康监测（BHM）中，裂缝识别和宽度测量是评估桥梁功能的两个最重要的指标。为了降低现场检测的人工成本，研究人员提出了多种基于深度学习（DL）的裂缝识别检测技术。然而，将这些技术推广到实际应用中仍然存在数据标注困难、模型泛化能力有限、实际裂缝宽度测量的DL识别不准确等问题。本文提出了一种面向应用的多阶段裂纹识别框架，即C onvolutional A ctive L Earning Identification - S分割-测量（ CAL -ISM）。它包括四个步骤：（1）基准分类模型的预训练，（2）半监督主动学习模型的再训练，（3）像素级裂缝分割，以及（4）裂缝宽度测量。除了数值实验之外，CAL-ISM 的性能还得到了实际应用的验证：(i) 桥柱试件和 (ii) 现场 BHM 项目。总之，从这些应用中获得的结果揭示了 CAL-ISM 在 BHM 应用中的巨大潜力，这在未来的 BHM 部署中被推荐。