Complementarity of Sensors and Weak Supervision for Defect Detection in Concrete Structures

Kasahara, Jun Younes Louhi; Yamashita, Atsushi; Asama, Hajime; Fujii, Hiromitsu

doi:10.1109/sii46433.2020.9026304

Cited by 5 publications

(8 citation statements)

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“…The method of Louhi Kasahara et al [15] employs both visual information and weak supervision, and it can be noted that this allows good performance in Cases 1 and 2, shown in Figure 9a,b. However, the results of Louhi Kasahara et al [15] were lacking in consistency, shown by high values of standard deviation. The addition of the proposed active query scheme in our proposed method allows again to raise the average performance as well as increase the consistency of output.…”

Section: Resultsmentioning

confidence: 99%

“…The methods of Louhi Kasahara et al [14] and Louhi Kasahara et al [15] were weakly supervised. Louhi Kasahara et al [14] only used weak supervision, as opposed to Louhi Kasahara et al [15] and the proposed method that use position information to complement weak supervision. The method of Louhi Kasahara et al [15] assumed that the human user would randomly select pairs of samples for weak supervision.…”

Section: Our Novelty and Overview Of Proposed Methodsmentioning

confidence: 99%

“…Louhi Kasahara et al [14] only used weak supervision, as opposed to Louhi Kasahara et al [15] and the proposed method that use position information to complement weak supervision. The method of Louhi Kasahara et al [15] assumed that the human user would randomly select pairs of samples for weak supervision. This resulted in weak supervision of inconsistent quality and, thus, inconsistent defect detection performance.…”

Section: Our Novelty and Overview Of Proposed Methodsmentioning

confidence: 99%

“…Figure 8 illustrates the defect detection outputs using the proposed method. Figure 9 reports the performance obtained using the method of Louhi Kasahara et al [14], RCA with the proposed active query method, the method of Louhi Kasahara et al [15], and the proposed method. In Figure 8a,b, it can be seen that the separation defect/non-defect hammering samples by our proposed method is excellent, only missing a couple of samples.…”

Section: Experimental Setup In Field Conditionsmentioning

confidence: 99%

“…From the point of view of concrete structure inspection, such approaches are also extremely attractive since human involvement can be maintained during the actual inspection process. The work in [14] proposed an initial framework for weakly supervised inspection of concrete structures, later expanded by the addition of visual information in [15]. However, weak supervision was assumed to be provided by the human user on randomly chosen pairs of samples.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Acoustic Inspection of Concrete Structures Using Active Weak Supervision and Visual Information

Kasahara

Yamashita

Asama

2020

Sensors

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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.

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Section: Resultsmentioning

confidence: 99%