COCO-Bridge: Structural Detail Data Set for Bridge Inspections
COCO-Bridge: Structural Detail Data Set for Bridge Inspections
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DOI：
10.1061/(asce)cp.1943-5487.0000949
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发表时间：
2021-05-01
影响因子：
6.9
通讯作者：
Hebdon, Matthew
中科院分区：
文献类型：
--
作者：
Bianchi, Eric;Abbott, Amos Lynn;Hebdon, Matthew
本研究的目的是提出一种方法，以解决两个问题所面临的无人机（UAV）在桥梁检测。第一个问题是，无人机在桥梁附近的操作是出了名的困难。这是因为操作员和无人机之间可能会丢失导航信号。因此，无人机检查过程的自动化或半自动化势在必行。提高自动化的一种方法是通过物体检测和物体回避来提高无人机对其环境进行情境化的能力。第二个问题是，据作者所知，无人机飞行期间或之后，还没有开发出自动将检测到的缺陷与结构桥梁细节联系起来的方法。对无人机检测裂纹和腐蚀等缺陷的能力进行了大量研究。然而，仅仅检测缺陷的存在并不能说明其重要性，也不能帮助检查员对特定结构桥梁细节进行评级。本文概述了用于检测关键结构桥梁细节的数据集和模型的用例，为增强自主无人机桥梁检测过程提供了背景和愿景。识别这些需要检查的结构桥梁细节可以帮助UAV在GPS拒绝的环境中进行路径规划和物体规避。结构细节的检测增加了将缺陷检测和问题定位到桥梁细节的能力。这对于在无人机飞行时向检查人员提供真实的缺陷易受影响区域的线索也有意义。收集了用于无人机目标检测的图像数据集--桥梁检测背景中的常见对象（COCO-Bridge），然后使用深度学习技术进行训练。该数据集由774张图像和2,500多个对象实例组成，用于检测4种结构桥梁细节：支座、盖板终端、节点板连接和平面外加劲肋。之所以选择这些细节，是因为它们要么必须由检查员进行评级，要么因为它们容易失败而进行检查。研究了通过图像增强来节省模型预测能力的方法，以扩展训练图像的性能。得出的结论是，对于这个数据域，结构桥梁细节图像，平均平均精度，和F1得分性能提高了沿其y轴镜像训练图像。本文的成果是一个开源的注释数据集，可用于计算机视觉应用中的视觉检测，提高人工智能在结构工程中的能力。(C)2021年美国土木工程师学会
The purpose of this research is to propose a means to address two issues faced by unmanned aerial vehicles (UAVs) during bridge inspection. The first issue is that UAVs have a notoriously difficult time operating near bridges. This is because of the potential for the navigation signal to be lost between the operator and the UAV. Therefore, there is a push to automate or semiautomate the UAV inspection process. One way to improve automation is by improving UAVs' ability to contextualize their environment through object detection and object avoidance. The second issue is that, to the best of the authors' knowledge, no method has been developed to automatically contextualize detected defects to a structural bridge detail during or after UAV flight. Significant research has been conducted on UAVs' ability to detect defects, like cracks and corrosion. However, detecting the presence of a defect alone does not contextualize its significance or help with an inspector's job to rate specific structural bridge details. This paper outlines a use case for a data set and model to detect critical structural bridge details, providing context and vision for enhancing the autonomous UAV bridge inspection process. Identifying these structural bridge details that require inspection may assist an UAV in path planning and object avoidance in GPS-denied environments. The detection of structural details adds an ability to contextualize defect detection and localize issues to a bridge detail. This also has implications for providing cues to inspectors, in real time, on defect-susceptible areas while UAVs are in flight. The image data set, Common Objects in Context for bridge inspection (COCO-Bridge), for UAV object detection was collected and then trained using deep learning techniques. This data set consists of 774 images and over 2,500 object instances to detect 4 structural bridge details: bearings, cover plate terminations, gusset plate connections, and out-of-plane stiffeners. These details were chosen because they either must be rated by an inspector or checked because they are prone to failure. Methods to economize the predictive capabilities of the model through image augmentation were investigated to extend the performance of the training images. It was concluded that for this domain of data, structural bridge detail images, the mean average precision, and F1 score performance were improved by mirroring the training images along their y-axis. The outcome of this paper was an open-source annotated data set, which can be used in computer vision applications for visual inspection, growing the capabilities of artificial intelligence in structural engineering. (C) 2021 American Society of Civil Engineers.