Multi-modal Classification Using Domain Adaptation for Automated Defect Detection Based on the Hammering Test
Multi-modal Classification Using Domain Adaptation for Automated Defect Detection Based on the Hammering Test
复制标题
使用域适应进行基于锤击测试的自动缺陷检测的多模态分类
DOI：
10.1109/sii52469.2022.9708607
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Asama Hajime
中科院分区：
文献类型：
--
作者：
Ushiroda Keitaro;Louhi Kasahara Jun Younes;Yamashita Atsushi;Asama Hajime
检查隧道等混凝土结构对于保持其安全性和耐久性非常重要。由于人工检测人员的短缺，对自动化检测系统的要求很高。锤击试验是一种常用的检测方法，以往的研究提出了自动化锤击试验系统。大多数工作基于机器学习模型来训练分类器，以识别锤击声，当训练数据不足以用于部署期间所考虑的数据时，这些工作就会受到影响。这个问题也被称为域间隙问题。在本文中，混凝土缺陷检测的方法，即使可用的训练数据收集从隧道，从实际检查隧道不同的建议。所提出的方法选择部分数据从检查目标隧道，其中标签是不可用的，使用沿着传统的标记训练数据在半监督支持向量机框架内的分类器的训练。该选择是通过整合来自普通相机的视觉信息和使用锤击测试获得的声学信息来进行的。实验结果表明，该方法在实验室条件下取得了令人满意的结果。
Inspecting concrete structures such as tunnels is very important to keep them safe and durable. Due to the shortage of human inspectors, automated system for inspection is highly required. Hammering test is one of the popular inspection methods, and previous studies proposed automated systems for hammering test. Most works based on machine learning models to train a classifier to recognize hammering sounds suffer when the training data is not adequate for the considered data during deployment. This problem is also known as domain gap problem. In this paper, a methodology for concrete defect detection even when the available training data was collected from a tunnel that differs from the actually inspected tunnel is proposed. The proposed method selects part of the data from the inspection target tunnel, for which labels are not available, to use along traditional labeled training data in the training of a classifier within the semi-supervised support vector machine framework. This selection is conducted using the integration of visual information from an ordinary camera and acoustic information obtained using the hammering test. Experimental results showed that the proposed method yielded satisfying results in the laboratory conditions.