This paper reports the application of deep learning for implementing the anomaly detection of defects on concrete structures, so as to facilitate the visual inspection of civil infrastructure. A convolutional autoencoder was trained as a reconstruction-based model, with the defect-free images, to rapidly and reliably detect defects from the large volume of image datasets. This training process was in the unsupervised mode, with no label needed, thereby requiring no prior knowledge and saving an enormous amount of time for label preparation. The built anomaly detector favors minimizing the reconstruction errors of defect-free images, which renders high reconstruction errors of defects, in turn, detecting the location of defects. The assessment shows that the proposed anomaly detection technique is robust and adaptable to defects on wide ranges of scales. Comparison was also made with the segmentation results produced by other automatic classical methods, revealing that the results made by the anomaly map outperform other segmentation methods, in terms of precision, recall, F₁ measure and F₂ measure, without severe under- and over-segmentation. Further, instead of merely being a binary map, each pixel of the anomaly map is represented by the anomaly score, which acts as a risk indicator for alerting inspectors, wherever defects on concrete structures are detected.

本文报告了深度学习在混凝土结构缺陷异常检测中的应用，以方便对民用基础设施进行视觉检查。卷积自动编码器被训练为基于重构的模型，具有无缺陷图像，可快速可靠地从大量图像数据集中检测缺陷。该培训过程是在无人监督的模式下进行的，不需要标签，因此不需要先验知识，并节省了大量标签准备时间。内置的异常检测器有助于最小化无缺陷图像的重建误差，这会导致缺陷的高重建误差，进而检测缺陷的位置。评估表明，所提出的异常检测技术是鲁棒的，并且可以适应各种规模的缺陷。还与其他自动经典方法产生的分割结果进行了比较，结果表明，在精度，查全率，F方面，异常图得出的结果优于其他分割方法。₁小节和F ₂小节，没有严重的分段不足和过度分段。此外，异常图的每个像素不仅是二进制图，还由异常分数表示，异常分数可在检测到混凝土结构缺陷时用作提醒检查员的风险指标。