Abstract Inspection of civil infrastructure is a major challenge to engineers due to the limitations in existing practice, which are as laborious, time-consuming and prone to error. To address these issues, we have applied deep learning for image-based inspection of concrete defects of civil infrastructure, and have established an artificial intelligence-empowered inspection pipeline methodology. This innovative approach comprises anomaly detection, anomaly extraction and defect classification. The anomaly detection and extraction are used to identify defect regions from the enormous volume of image datasets, which used to be the common challenges encountered in automated visual inspections. The search space of defects is substantially reduced, i.e., at least 60% of the original volume of image datasets, with an average hit rate of ~88.7% and an average false alarm rate of ~14.2%. Following that, deep learning-based classifiers are used to categorize defects into appropriate classes. The assessment results show that the proposed inspection pipeline exhibits great capability in detecting, extracting and classifying defects subjected to various environmental and operational conditions, including lighting condition, camera distance and capturing angle, with an average testing accuracy of 95.6%.

中文翻译：

---

基于人工智能的混凝土结构图像检测管道

摘要 民用基础设施的检查是工程师面临的主要挑战，由于现有实践的局限性，其费力、费时且容易出错。为了解决这些问题，我们将深度学习应用于民用基础设施混凝土缺陷的基于图像的检测，并建立了人工智能赋能的检测管道方法。这种创新方法包括异常检测、异常提取和缺陷分类。异常检测和提取用于从大量图像数据集中识别缺陷区域，这曾经是自动化视觉检测中遇到的常见挑战。缺陷的搜索空间大大减少，即至少是原始图像数据集体积的 60%，平均命中率约为 88。7%，平均误报率为~14.2%。之后，使用基于深度学习的分类器将缺陷分类为适当的类别。评估结果表明，所提出的检测管道在各种环境和操作条件下（包括照明条件、相机距离和捕获角度）下具有良好的检测、提取和分类缺陷的能力，平均检测准确率为95.6%。