Method and system for detecting concrete slump at discharge port of concrete mixer

By using video image processing and fluid dynamics methods, the sparse flow field and three-dimensional model of the concrete mixer truck unloading port are extracted, solving the problems of low efficiency and low accuracy of manual detection in the existing technology, and realizing high-precision real-time slump detection.

CN121032966APending Publication Date: 2025-11-28CHINA CONSTR FIFTH ENG DIV CORP LTD
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Patent Information

Application Number
CN202511152205.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-18
Publication Date
2025-11-28

AI Technical Summary

Technical Problem

In existing technologies, slump detection at the unloading port of concrete mixer trucks relies on manual operation, which is inefficient and cannot achieve real-time online monitoring. Furthermore, traditional image segmentation algorithms are affected by changes in lighting and the texture of the concrete surface, resulting in low detection accuracy.

Method used

By employing video image processing methods, feature descriptors are extracted from local binary pattern texture and motion history information to generate an initial displacement vector field. Abnormal matching points are eliminated by combining fluid dynamics principles, a sparse matching flow field is constructed, a geodesic contour model is guided, the concrete edge is segmented, a three-dimensional surface model is constructed, and the slump value is determined by combining geometric features.

Benefits of technology

It achieves high-precision real-time detection of the slump at the unloading port of concrete mixer trucks, reducing manual labor intensity and improving the accuracy and stability of detection.

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Abstract

The invention provides a concrete slump detection method and system for a discharge port of a concrete mixer, and the method comprises the steps: obtaining a video image sequence of a discharge port region, extracting spatial-temporal feature points from continuous video frames, carrying out the matching of a feature descriptor fusing local binary pattern texture and motion historical information, and generating an initial displacement vector field; local decomposition is carried out on the initial displacement vector field, abnormal matching points are eliminated according to a hydrodynamic incompatibility threshold value of velocity divergence and rotation, and a sparse matching flow field is obtained; constructing a geodesic line active contour model guided by the sparse matching flow field, and iteratively segmenting a concrete edge contour; taking the edge contour as a boundary, taking a velocity vector of the sparse matching flow field as a surface gradient, adopting a Poisson reconstruction algorithm to construct a three-dimensional surface model of the concrete accumulation body, and based on the projection area, the centroid height and the volume of the three-dimensional surface model, and combining a pre-calibrated mapping relation between geometric characteristics and slump, calculating the slump of the concrete accumulation body. And determining the concrete slump value.
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