Title: Weakly Supervised Defect Detection using Acoustic Data based on Positive and Negative Constraints
Abstract:Weakly Supervised Defect Detection using Acoustic Data based on Positive and Negative Constraints Jun Younes Louhi Kasahara, Atsushi Yamashita and Hajime Asama Pages 1331-1336 (2020 Proceedings of the...Weakly Supervised Defect Detection using Acoustic Data based on Positive and Negative Constraints Jun Younes Louhi Kasahara, Atsushi Yamashita and Hajime Asama Pages 1331-1336 (2020 Proceedings of the 37th ISARC, Kitakyushu, Japan, ISBN 978-952-94-3634-7, ISSN 2413-5844) Abstract: Concrete structures are heavily used in most modern societies and the population of structures in need of inspection is rapidly growing. On the other hand, the manpower for inspection is decreasing. This has brought into focus the need for automated inspection methods for concrete structures. The hammering test is a popular method for inspection that uses the sound resulting from a hammer impact on the surface of the structure for defect detection. Previous methods largely employed machine learning approaches for the automation of the hammering test. Weakly supervised methods used positive queries answers on sample pair similarity: a human user was questioned on the similarity of pairs of hammering samples and similar pairs were used to transform the feature space. However, it can be expected that dissimilar pairs would also be gathered in this process. Therefore, in the present paper is proposed a method for weakly supervised defect detection in concrete structures using hammering with both positive and negative answers to queries. After the initial feature space transformation based on positive query answers, another feature space transformation is introduced based on negative query answers. Experiments in laboratory conditions showed the effectiveness of the proposed method. Keywords: Defect detection; Infrastructure inspection; Weak supervision; Acoustic data; Clustering DOI: https://doi.org/10.22260/ISARC2020/0183 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to MendeleyRead More
Publication Year: 2020
Publication Date: 2020-10-14
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 1
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