Viola-Jones Algorithm for Automatic Detection of Hyperbolic Regions in GPR Profiles of Bridge Decks

Mohammed Abdul Rahman, T. Zayed
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引用次数: 5

Abstract

Ground Penetrating Radar (GPR) is widely utilized as a Non-destructive technique by transportation authorities for inspection of bridge decks due to its ability to identify major subsurface defects in a short span of time. The attenuation of recorded signal at rebar level form a characteristic hyperbolic shape in profiles obtained from GPR scans and corresponds to the corrosiveness state of concrete. The detection of these hyperbolic regions is of paramount importance and is a precursor to successful interpretation of GPR data. This paper aims to automate the detection of hyperbolic regions or hyperbolas in GPR profiles based on Viola-Jones Algorithm. A custom detector is obtained through training with numerous samples of hyperbolas over multiple stages. The detection is achieved through the developed detector and it was applied over a complete bridge deck for validation purpose. The eventual goal of such detection is to facilitate the automation of GPR data analysis.
桥面GPR剖面双曲区域自动检测的Viola-Jones算法
由于探地雷达(GPR)能够在短时间内识别出主要的地下缺陷,因此它作为一种无损检测技术被交通部门广泛应用于桥面检测。在探地雷达扫描得到的剖面中,钢筋水平记录信号的衰减形成了一个特征的双曲线形状,与混凝土的腐蚀状态相对应。这些双曲线区域的探测至关重要,是探地雷达数据成功解释的前兆。本文旨在基于Viola-Jones算法实现探地雷达剖面中双曲区域或双曲线的自动检测。通过对多个阶段的大量双曲线样本进行训练,获得自定义检测器。通过开发的检测器实现检测,并将其应用于完整的桥面以进行验证。这种检测的最终目标是促进探地雷达数据分析的自动化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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