Abstract：
Deep learning techniques have offered innovative and efficient tools for accurate and automated detection of sewer defects by leveraging large-scale sewer data and advanced feature learning algorithms. However, there has been a lack of thorough characterization of the geometric properties of segmented defects, let alone systematically calculate the severity level of sewer defects and quantitatively evaluate their impacts on flood conditions in hydrodynamic models. This study proposed a comprehensive framework and related metrics to accurately and automatically detect, segment, characterize, and evaluate the impacts of sewer defects on flooded nodes and volumes by integrating a DeepLabv3+-based segmentation technique, an automated geometric characterization and severity quantification module, and a GIS and SWMM-based hydrodynamic modeling. The results clearly showed in details where and how much the urban flooding was affected by the different defect types. The segmentation model achieved satisfactory detection performance, with mean pixel accuracy (MPA), mean intersection over union (MIoU), and frequency weighted intersection over union (FWIoU) of 0.99, 0.74 and 0.95, respectively. In terms of severity level quantification, there were 98%, 90%, 90% and 83% of predictions consistent with real conditions for falling off, obstacle, disjoint and leakage. It was shown that the number of surcharging manholes and total flood volume (TFV) were greatly affected by sewer defects, with over 16% increase in TFVs under all investigated rainfall events. The results addressed the impacts of sewer defects on urban flooding and demonstrated the powerful tools provided by the proposed framework for decision-making on sewer defect detection and management.
摘要：
深度学习技术通过利用大规模下水道数据和高级特征学习算法，为下水道缺陷的准确和自动化检测提供了创新和高效的工具。然而,对分段缺陷的几何特性缺乏彻底的表征，更不用说系统地计算下水道缺陷的严重程度，并在水动力模型中定量评估其对洪水条件的影响。这项研究提出了一个全面的框架和相关指标，以准确和自动地检测，段,表征，并通过集成基于DeepLabv3的分割技术来评估下水道缺陷对淹没节点和体积的影响，自动几何表征和严重程度量化模块，以及基于GIS和SWMM的水动力建模。结果清楚地显示了不同缺陷类型对城市洪水的影响程度。分割模型取得了令人满意的检测性能,具有平均像素精度(MPA)，联合平均交点(MIoU)，和频率加权的并集交点(FWIoU)分别为0.99、0.74和0.95。就严重程度量化而言，有98%，90%,90%和83%的预测与实际脱落条件一致，障碍,脱节和泄漏。结果表明，下水道缺陷极大地影响了蓄水井数和总洪水量（TFV），在所有调查的降雨事件下，TFV增加了16%以上。结果解决了下水道缺陷对城市洪水的影响，并证明了拟议的下水道缺陷检测和管理决策框架提供的强大工具。
