Improvement of environmental adaptivity of defect detector for hammering test using boosting algorithm
Improvement of environmental adaptivity of defect detector for hammering test using boosting algorithm
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DOI：
10.1109/iros.2015.7354307
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发表时间：
2015-12
期刊：
影响因子：
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通讯作者：
Hiromitsu Fujii;A. Yamashita;H. Asama
中科院分区：
文献类型：
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作者：
Hiromitsu Fujii;A. Yamashita;H. Asama
自动化诊断方法对于维护陈旧的社会基础设施是必要的。在此背景下，锤击试验是一种有效的检测方法，由于其结果的准确性和操作效率而被广泛使用。虽然锤击检测方法的机器人自动化是非常必要的，但开发能够在实际检测现场运行的自动诊断算法是必不可少的。此外，诊断算法的可移植性也是非常理想的。在这项研究中，为了构建可靠的检测器并提高它们对锤击测试性能的便携性，我们提出了一种基于Boosting的缺陷检测器，该检测器对环境条件的变化具有健壮性。具体地说，我们提出了一种利用锤击声提取的特征值的精化及其评价函数的模板向量的更新规则来构造抗噪分类器的方法。我们在混凝土隧道中的实验结果证明了该方法的有效性，证实了该分类器在实际现场的准确性和对环境噪声的适应性。
An automated diagnosis methodology is necessary for the maintenance of superannuated social infrastructures. In this context, the hammering test is an efficient inspection method, and it has been widely used because of the resulting accuracy and efficiency of operation. While robotic automation of the hammering inspection method is highly desirable, the development of an automatic diagnostic algorithm that can operate at actual inspection sites is essential. Furthermore, portability of the diagnostic algorithm is also highly desirable. In this study, in order to construct reliable detectors and to improve their portability for the performance of the hammering test, we propose a boosting-based defect detector that is robust against variations in environmental conditions. In particular, we present the construction of a noise-robust classifier with a refinement of the feature values extracted from hammering sounds and an updating rule of template vectors of its evaluation function. Our experimental results in a concrete tunnel demonstrate the effectiveness of the proposed method; the accuracy of the classifier at an actual site and adaptivity to environmental noise are confirmed.