A method for detecting subsidence using simple linear iterative clustering

By using a simple linear iterative clustering method to segment images from laser scanning data of the road surface, the subjectivity and accuracy issues of subsidence detection in existing technologies are resolved. This enables rapid and accurate identification and depth calculation of subsidence areas, and is highly adaptable to different road surface features and instrument conditions.

CN122089649APending Publication Date: 2026-05-26WEI LE TECHNOLOGY GROUP CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
WEI LE TECHNOLOGY GROUP CO LTD
Filing Date
2026-01-09
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing technologies for detecting asphalt pavement subsidence suffer from problems such as high subjectivity, poor accuracy, low efficiency, lack of comprehensiveness, and difficulty in data quantification, resulting in the inability to accurately identify subsidence areas and depths, thus affecting road safety and lifespan.

Method used

A simple linear iterative clustering method is used to segment the laser scanning data of the road surface. The category of the pixel is determined by the comprehensive distance of gray value and spatial distance, which can quickly identify the subsidence area and calculate its relative height difference to eliminate the influence of slope and determine the subsidence depth.

Benefits of technology

It enables rapid and accurate identification and depth calculation of subsidence areas, improving the flexibility and accuracy of detection, adapting to different road surface features and changes in instrument status, and eliminating the need for a large amount of labeled data and predefined templates.

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Abstract

This invention discloses a method for detecting subsidence using simple linear iterative clustering, applicable to the road infrastructure industry. It can simply, accurately, and efficiently acquire subsidence information on asphalt pavements. Step 1: Scaling the image; Step 2: Dividing the image into multiple small regions using simple linear iterative clustering, significantly reducing the relative weight of spatial and gray-level distance to obtain a reasonable number and shape of regions; Step 3: Analyzing the hierarchical relationships between regions based on their geometric features to establish inclusion pairs and filtering out non-target regions; Step 4: Performing local planar fitting on the periphery of the remaining bottom-level sub-regions to eliminate the influence of pavement slope on subsidence depth calculation; Step 5: For the remaining bottom-level sub-regions, calculating the gray-level difference between each point on the outer edge of the sub-region and the center of the sub-region, and determining whether it is a subsidence area based on the proportion of depth differences exceeding a threshold. This invention can quickly segment images and identify potential subsidence areas.
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