Image processing-based water supply pipeline defect detection method and system

By combining image processing and fluid dynamics models, the problems of low efficiency and insufficient accuracy in water supply pipeline inspection have been solved, achieving high-precision defect identification and quantitative assessment, and providing a scientific basis for water supply network renovation.

CN122156193BActive Publication Date: 2026-07-21CHINA RAILWAY GUIZHOU ENG CORP LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CHINA RAILWAY GUIZHOU ENG CORP LTD
Filing Date
2026-05-06
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Existing methods for inspecting water supply pipelines are inefficient, subjective, difficult to quantify and evaluate, result in decreased image contrast, blurred details, large dynamic distortion, low detection accuracy, and inaccurate defect classification.

Method used

A defect detection method for water supply pipelines based on image processing is adopted, including preprocessing, multi-scale feature extraction, fluid dynamics inversion model and comprehensive quantitative evaluation. The method improves the YOLOv8 network for defect identification by using atmospheric scattering model to eliminate fog and optical flow field to correct the image. A comprehensive defect index is constructed by combining static geometric features and dynamic influence factors.

Benefits of technology

It improves image clarity and defect identification accuracy, quantifies defect levels, and provides a scientific basis for pipeline renovation decisions.

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Abstract

The application discloses a water supply pipeline defect detection method and system based on image processing, and relates to the technical fields of image processing and defect detection. The method comprises the following steps: acquiring an original image of the inner wall of a water supply pipeline, and performing pretreatment to obtain a standardized image; constructing a defect candidate region preliminary screening network based on multi-scale feature extraction, identifying and locating potential defect regions in the image; extracting static geometric features of the potential defect regions; based on a fluid dynamics inversion model, combining continuous multiple frames of images and real-time operation parameters of the pipeline, calculating dynamic influence factors of the potential defect regions under the influence of water flow; and fusing the static geometric features and the dynamic influence factors to construct a defect comprehensive quantitative evaluation model to determine the grade of the defect. Through the construction of the pretreatment, multi-dimensional feature fusion network and defect growth potential function, the application solves the problems of high-precision detection and quantitative evaluation in a complex pipeline environment.
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