Department of Precision Engineering, The University of Tokyo, Tokyo 113-8656, Japan.
Sensors (Basel). 2020 Jan 23;20(3):629. doi: 10.3390/s20030629.
Concrete structures are featured heavily in most modern societies. In recent years, the need to inspect those structures has been a growing concern and the automation of inspection methods is highly demanded. Acoustic methods such as the hammering test are one of the most popular non-destructive testing methods for this task. In this paper, an approach to defect detection in concrete structures with active weak supervision and visual information is proposed. Based on audio and position information, pairs of samples are actively queried to a user on their similarity. Those are used to transform the feature space into a favorable one, in a weakly supervised fashion, for clustering defect and non-defect samples, reinforced by position information. Experiments conducted in both laboratory conditions and in field conditions proved the effectiveness of the proposed method.
混凝土结构在大多数现代社会中都占有重要地位。近年来，对这些结构进行检查的需求日益增长，因此对自动化检查方法的需求也很高。声学方法，如敲击测试，是这项任务中最受欢迎的无损检测方法之一。本文提出了一种基于主动弱监督和视觉信息的混凝土结构缺陷检测方法。该方法基于音频和位置信息，主动向用户查询相似性对样本。这些样本用于在弱监督的情况下，通过位置信息增强，将特征空间转换为有利于聚类缺陷和非缺陷样本的空间。在实验室条件和现场条件下进行的实验证明了该方法的有效性。
