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Underground drainage pipeline disease segmentation method based on full convolutional neural network

A convolutional neural network and underground drainage technology, applied in the interdisciplinary field, can solve the problems of low detection accuracy and efficiency, poor robustness and generalization ability of underground drainage pipeline diseases, and achieve poor training accuracy and misjudgment and missed judgment, increase the effect of utilization

Pending Publication Date: 2021-01-22
ZHENGZHOU UNIV +1
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Problems solved by technology

[0004] Aiming at the deficiencies of the prior art, the purpose of the present invention is to provide a method for segmenting underground drainage pipeline diseases based on a fully convolutional neural network to solve the problem of low detection accuracy and efficiency, robustness and pan- The problem of poor ability

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  • Underground drainage pipeline disease segmentation method based on full convolutional neural network
  • Underground drainage pipeline disease segmentation method based on full convolutional neural network
  • Underground drainage pipeline disease segmentation method based on full convolutional neural network

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[0035]In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. The preferred embodiments of the invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present invention more thorough and comprehensive.

[0036]Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments, and are not intended to limit the present invention.

[0037]Such asfigure 1 As shown, the present invention provides an underground drainage pipe...

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Abstract

The invention is applicable to the technical field of interdisciplinary of deep learning and underground pipe gallery engineering, and relates to an underground drainage pipeline disease segmentationmethod based on a full convolutional neural network, which comprises the following steps: acquiring an underground drainage pipeline disease data set; making a drainage pipeline disease data set; optimizing a semantic segmentation algorithm; making model hyper-parameter adjustment; performing model training; verifying a model; and testing the model. A deep learning algorithm is adopted, an FCN full convolutional neural network is optimized, a semantic segmentation method suitable for complex and similar disease characteristics of the underground drainage pipeline is researched and developed, and real underground drainage pipeline disease detection big data is adopted, so that segmentation of the disease pixel level of the underground drainage pipeline is realized, better robustness and generalization ability are achieved, and the invention is suitable for large-scale popularization and application. Precision and efficiency of underground drainage pipeline disease detection are effectively improved.

Description

Technical field[0001]The invention belongs to the cross-disciplinary technical field of deep learning and underground pipe gallery engineering, and in particular relates to a method for dividing underground drainage pipe diseases based on a full convolutional neural network.Background technique[0002]In recent years, the potential safety hazards caused by the aging and disrepair of underground drainage pipelines have become prominent. Leakage, cracking, corrosion, subsidence and other diseases are widespread, causing frequent accidents such as environmental pollution, urban waterlogging, and road collapses, which seriously affect the daily life of residents and cause serious problems. Heavy casualties and economic losses. Therefore, the routine inspection of underground pipelines and the detection of typical pipeline diseases are of great significance for the repair and reinforcement of underground pipelines and safe operation and maintenance.[0003]However, the urban drainage pipe ne...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/10G06V10/764
CPCG06T7/0004G06T7/10G06T2207/10016G06T2207/20081G06T2207/20084G06V20/10G06V10/454G06N3/084G06V10/764G06N3/045G06N3/08G06F18/2163G06F18/214G06F18/2193
Inventor 王念念方宏远胡群芳薛冰寒杜雪明黄帆
Owner ZHENGZHOU UNIV
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