This paper presents a framework for automated defect inspection of the concrete structures, made up of data collection, defect detection, scene reconstruction, defect assessment and data integration stages. A mobile data collection system, comprising a 360° camera and a digital Light Detection and Ranging (LiDAR), is developed to render high flexibility of data acquisition of image and three-dimensional spatial data, while users traverse complex indoor environments. Deep learning algorithms are implemented to efficiently detect defects from the collected images, and a simultaneous localization and mapping algorithm is adopted for site reconstruction with the acquired LiDAR data. Based on the images of detected defects, assessment is conducted to evaluate the defect conditions, complemented with the defect dimensions estimated from the aligned image and LiDAR data. The position of defects could also be identified and mapped to respective structural elements. All the inspection results are finally integrated into existing Building Information Modelling files for better facility management. The proposed workflow was validated using a case study for determining concrete cracks and spalls in a real-world facility, successfully demonstrating the joint application of advanced technologies in facilitating inspection programs of civil infrastructure.

本文提出了一个混凝土结构自动缺陷检测框架，由数据收集、缺陷检测、场景重建、缺陷评估和数据集成阶段组成。开发了由360°摄像头和数字光探测和测距（LiDAR）组成的移动数据采集系统，旨在为用户在复杂的室内环境中进行图像和三维空间数据的数据采集提供高度灵活性。实施深度学习算法以有效地从收集的图像中检测缺陷，并采用同步定位和映射算法对获取的 LiDAR 数据进行站点重建。根据检测到的缺陷图像，进行评估以评估缺陷状况，辅以从对齐图像和激光雷达数据估计的缺陷尺寸。缺陷的位置也可以被识别并映射到相应的结构元素。所有检查结果最终都集成到现有的建筑信息模型文件中，以便更好地管理设施。所提议的工作流程使用案例研究进行了验证，该案例研究用于确定真实世界设施中的混凝土裂缝和剥落，成功展示了先进技术在促进民用基础设施检查计划方面的联合应用。