Cloud system for automatic identification and detection of underground pipe network based on deep learning

Abstract

The invention discloses a cloud system for automatic identification and detection of underground pipe networks based on deep learning, including an underground pipe network defect collection module, a public data set collection module, a typical defect sample library of the underground pipe network, a typical defect analysis and classification module, and an underground network management installation. Environmental geological state data collection module, deep learning module, automatic identification and detection module of underground pipe network and decision-making module; by establishing a typical defect sample library of underground pipe network, using the known multiple underground pipes contained in the typical defect sample library of underground pipe network The typical defects of the network and the data collected by the public data set collection module are used as the data set for model training. The automatic identification and detection of the underground pipe network obtained after training The LexNet network model has high identification and detection accuracy, and the automatic identification and detection speed of the underground pipe network is fast. The test results are of stable quality and high reliability.

Description

technical field [0001] The invention relates to the technical field of automatic detection of underground pipe networks, in particular to a cloud system for automatic identification and detection of underground pipe networks based on deep learning. Background technique [0002] During the construction and operation of the underground pipeline network, pipeline damage and deformation occur from time to time, and the structural and functional defects of the pipeline caused by uneven settlement and environmental factors make the drainage pipeline unable to play its due role. When the rainstorm hits, the rainwater cannot be removed in time, and the big cities often become a swamp country. A large amount of groundwater flows into the pipeline through the seepage interface, and the sediment flows out, causing the surrounding of the pipeline to be hollowed out, and the bearing capacity of the pipeline foundation decreases, causing disconnection and dislocation, and even more voids ...

AI Technical Summary

Problems solved by technology

[0003] The traditional underground pipe network detection method still relies on manual detection. First, the defect graphics inside the underground pipe network are collected, and then the image defects are identified and attribute entered manually. The detection mode not only has a large workload and low operating efficiency, but also easily causes personnel Judgment errors due to visual fatigue

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