Particle distribution detection method and device based on particle density

By estimating particle density and adaptively adjusting cutting parameters through a deep learning image segmentation model, combined with global coordinate system transformation and deduplication algorithms, the problem of balancing particle detection accuracy and efficiency in existing technologies is solved, and efficient and accurate particle distribution detection is achieved.

CN120635005APending Publication Date: 2025-09-12BEIJING BESTPOWER INTELCONTROL TECH CO LTD
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Patent Information

Application Number
CN202510721987.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-30
Publication Date
2025-09-12

AI Technical Summary

Technical Problem

When processing large-size, high-density particle images, existing technologies have difficulty balancing detection accuracy and computational efficiency. The fixed overlapping cutting method leads to a high particle missed detection rate, serious waste of computing resources, and insufficient adaptability.

Method used

Particle density is estimated through a pre-trained deep learning image segmentation model, image segmentation parameters are adaptively adjusted, and the image segmentation method is optimized by combining global coordinate system transformation and deduplication algorithms to reduce missed particle detections and improve detection efficiency.

Benefits of technology

It realizes the dynamic adjustment of cutting parameters according to particle density, improves the accuracy and efficiency of particle distribution detection, and is suitable for real-time processing of high-resolution images.

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Abstract

The invention relates to a particle density-based particle distribution detection method. The method comprises the following steps: acquiring a first image containing the distribution of a plurality of macroscopic rock particles; estimating the particle density of the first image through a pre-trained deep learning image segmentation model; cutting the first image into a plurality of second images according to a cutting step length, wherein the cutting step length is determined by an overlapping value and the size of the second image; performing target detection on the second image to obtain a bounding box and mask information of the macroscopic rock particles; and removing a result of repeated detection across the second image through a deduplication algorithm, and generating final particle distribution data. The invention further provides a corresponding particle distribution detection device based on the particle density. According to the particle distribution detection method, the image cutting parameters can be adaptively adjusted according to the particle density, and the detection precision and the calculation efficiency are considered at the same time.
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