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Automated bridge component recognition from point clouds using deep learning
Structural Control and Health Monitoring ( IF 5.4 ) Pub Date : 2020-06-20 , DOI: 10.1002/stc.2591
Hyunjun Kim 1 , Jinyoung Yoon 2 , Sung‐Han Sim 1
Affiliation  

To assess the current conditions of bridges in practice, a manual visual inspection is commonly used for maintenance purposes. Because manual visual inspection is time consuming, expensive, and laborious, computer vision with deep learning techniques has recently been introduced as a promising tool for automatic damage detection and classification. However, damage localization is difficult in that close‐up images do not contain the global structural context, while knowing the damage locations and the associated structural components is essential for interpreting the overall structural health. Compared with two‐dimensional (2D) image data, the point clouds in the three‐dimensional (3D) space with an extra dimension can be useful for bridge component recognition. However, previous research employing geometric features of bridges was only partially successful for relatively simple types of bridges without the background regions (e.g., ground, water, and vegetation). This study presents a methodology for automated bridge component recognition using deep learning. The proposed approach is designed for general bridges that may have curved decks or different pier heights. Furthermore, the proposed method can handle point clouds that have points in the background regions, significantly reducing the time‐consuming preprocessing of the point cloud. For robust and automated segmentation, a set of point clouds is extracted from a bridge by subspace partition, and a deep leaning technique is employed to classify labels. Subsequently, the classification results are combined to determine the consensus label for each point based on a majority of estimated classes, thereby improving identification accuracy. The classification performance is experimentally validated using full‐scale bridge.

中文翻译:

使用深度学习从点云自动识别桥梁组件

为了在实践中评估桥梁的当前状况,通常使用手动外观检查来进行维护。由于手动视觉检查非常耗时,昂贵且费力,因此最近已引入具有深度学习技术的计算机视觉,作为自动损坏检测和分类的有前途的工具。但是,由于特写图像不包含全局结构上下文,因此很难确定损坏的位置,而了解损坏的位置和相关的结构组件对于解释整体结构健康至关重要。与二维(2D)图像数据相比,三维(3D)空间中具有额外维度的点云可用于识别桥梁零件。然而,对于没有背景区域(例如,地面,水和植被)的相对简单类型的桥梁,先前采用桥梁几何特征的研究仅获得部分成功。这项研究提出了一种使用深度学习自动识别桥梁构件的方法。建议的方法是为可能具有弯曲甲板或不同桥墩高度的普通桥梁设计的。此外,所提出的方法可以处理在背景区域中具有点的点云,从而大大减少了点云的耗时预处理。为了进行可靠且自动的分割,通过子空间划分从桥中提取了一组点云,并采用了深度学习技术对标签进行分类。后来,结合分类结果,根据大多数估计类别确定每个点的共识标签,从而提高识别准确性。使用全尺寸电桥对分类性能进行了实验验证。
更新日期:2020-06-20
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