The regular detection and diagnosis mechanism for the silting disease of drainage pipelines
(SDP) is critical for making dredging decisions and flood forecasting. Simultaneously, there …

An accurate and robust diagnostic model for the silting disease of drainage pipelines is
significant for the numerical simulation of urban waterlogging and assessment of risk areas …

During the process of urban development, there is large‐scale laying of underground
pipeline networks and coordinated operation of both new and old networks. The …

To address the issues of low automation, reliance on manual screening by professionals,
and long detection cycles in current urban drainage pipeline defect detection, this study …

The inspection of water conveyance tunnels plays an important role in water diversion
projects. Siltation is an essential factor threatening the safety of water conveyance tunnels …

Deep learning algorithms have achieved encouraging results for pipeline defect
segmentation. However, existing defect segmentation methods may encounter challenges in …

Sanitary sewer systems are major infrastructures in every modern city, which are essential in
protecting water pollution and preventing urban waterlogging. Since the conditions of sewer …

We propose deep learning-based models for the risk detection of underground pipelines. To
build effective diagnosis models, we construct two types of deep neural network frameworks …

Detecting a pipeline's abnormal status, which is typically a blockage and leakage accident,
is important for the continuity and safety of mine backfill. The pipeline system for gravity …

Regular detection of defects in drainage pipelines is crucial. However, some problems
associated with pipeline defect detection, such as data scarcity and defect counting difficulty …