Crack assessment of reinforced concrete structures using stereo cameras is a potential way for increasing the efficiency and safety of infrastructure maintenance routines. However, existing damage methods for reinforced concrete structures are based on the segmentation of two-dimensional planes without consideration to the actual size of concrete damage. Furthermore, on-site structural monitoring requires the installation of a large number of contact-based sensing devices, resulting in the potentially excessive consumption of time and financial resources. Therefore, a new vision-based damage assessment method for reinforced concrete structures using a novel intelligent inspection robot with Internet of things–enabled data communication system is proposed in this article. In the first part of this article, the data acquisition system of the inspection robot and the algorithm for three-dimensional structural reconstruction using a stereo camera is discussed. The discussion is followed by a description of the method for crack quantification based on a new proposed deep-learning technique. Finally, to accomplish damage localization, the quantified concrete damage with actual size information is projected onto a three-dimensional surface point cloud reconstruction of the inspected structure. To verify the proposed method, a reinforced concrete column that has undergone cyclic loading failure is used as an inspection subject. The validation experiment demonstrated the ability of the proposed system to segment, localize, and quantify the damage in three-dimensional space with high accuracy.

使用立体摄像机对钢筋混凝土结构进行裂缝评估是提高基础设施维护例行程序的效率和安全性的一种潜在方法。但是，现有的钢筋混凝土结构损伤方法是基于二维平面的分割，而不考虑混凝土损伤的实际大小。此外，现场结构监控要求安装大量基于接触的传感设备，从而可能导致时间和财务资源的过度消耗。因此，本文提出了一种新的基于视觉的钢筋混凝土结构损伤评估方法，该方法是使用具有物联网功能的数据通信系统的新型智能检查机器人。在本文的第一部分，讨论了检查机器人的数据采集系统和利用立体摄像机进行三维结构重构的算法。讨论之后将描述基于新提出的深度学习技术的裂纹量化方法。最后，为了完成损伤定位，将具有实际尺寸信息的量化混凝土损伤投影到受检结构的三维表面点云重构上。为了验证所提出的方法，将经历了周期性荷载破坏的钢筋混凝土柱用作检查对象。验证实验证明了所提出系统对三维空间中的损伤进行细分，定位和量化的能力，具有很高的准确性。