Weakly Supervised Acoustic Defect Detection in Concrete Structures Using Clustering-Based Augmentation
Weakly Supervised Acoustic Defect Detection in Concrete Structures Using Clustering-Based Augmentation
复制标题
基于聚类增强的混凝土结构弱监督声缺陷检测
DOI：
10.1109/tmech.2021.3077496
复制
发表时间：
2021-12-01
影响因子：
6.4
通讯作者：
Asama, Hajime
中科院分区：
文献类型：
--
作者：
Kasahara, Jun Younes Louhi;Fujii, Hiromitsu;Asama, Hajime
混凝土结构检测方法的自动化是一个世界性的紧迫问题。弱监督方法，即以传统类别标签以外的其他形式的监督为基础的方法，提供了自动化和人工参与的独特组合，对于检查工作等关键任务非常有效。与为有监督的学习方法生成训练数据相比，生成弱监督没有那么乏味。然而，由于它的信息量较少，因此往往需要大量薄弱的监督。在实践中，经常出现的情况是，只有数量稀少的薄弱监管可用。在这篇文章中，我们提出了一种新的弱监督混凝土结构声学缺陷检测方法，增强了人为的弱监督。在实验室和现场条件下的实验表明，所提出的方法在低监控量和弱监控量的情况下允许显著的性能改进。
The automation of inspection methods for concrete structures is a pressing issue worldwide. Weakly supervised approaches, i.e., approaches based on supervision in other forms than traditional class labels, offer a unique mix of automation and human involvement that is highly effective for critical tasks such as inspection work. Generating weak supervision is less tedious than generating training data for supervised learning approaches. However, since it is less informative, high amounts of weak supervision are often needed. In practice, it is often the case that only scarce amounts of weak supervision are available. In this article, we propose a novel approach for weakly supervised acoustic defect detection in concrete structures that augment human-provided weak supervision. Experiments in both laboratory and field conditions showed that the proposed method allows for considerable performance gains for low amounts of weak supervision.