Method for identifying needle-like and flaky aggregate particles based on multi-view image fusion

The method addresses the inaccuracy of single-view angle image analysis by fusing multiple view angles with deep learning to enhance the detection of needle and flaky aggregates, improving recognition accuracy through three-dimensional shape feature integration.

CN115471702BActive Publication Date: 2025-07-15SOUTHEAST UNIV
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Patent Information

Application Number
CN202211150991.2
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-09-21
Publication Date
2025-07-15
Estimated Expiration
2042-09-21

AI Technical Summary

Technical Problem

The existing aggregate multi-view morphology utilization rate is low, resulting in low accuracy in recognition of needle sheet aggregates. The shape characteristics of artificially extracted aggregate particles depend on the experience of researchers, and are subjective and easy to ignore some effective information.

Method used

Using a method based on multi-view image fusion, the aggregate drop is controlled by a vibrating feeder, aggregate image sequences from multiple perspectives are collected, image preprocessing and multi-morphological data are performed, and three-dimensional morphological characteristics of aggregate are constructed using deep learning models, and shape classification is performed by combining deep residual networks.

Benefits of technology

The recognition accuracy of needle sheet aggregate is improved, effective characterization of the three-dimensional form of aggregate is realized, manual intervention is reduced, and detection efficiency and accuracy are improved.

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Abstract

The present invention provides a method for identifying flaky and elongated aggregate particles based on multi-view image fusion, including: collecting an aggregate image sequence from multiple perspectives, preprocessing the images and performing multi-morphology data association of the same aggregate particle, adjusting the angle of the axis where the minimum moment of inertia of the aggregate particle is located, fusing the multi-view morphologies of the aggregate particle into one aggregate image, establishing an aggregate shape classification model based on a deep learning network and classifying the aggregate shape. By fusing the aggregate morphologies in different perspectives, the present invention constructs a multi-view fusion image capable of reflecting the three-dimensional morphological characteristics of the aggregate, improves the three-dimensional morphological representation ability of the two-dimensional aggregate image, and uses the adaptive feature extraction ability of the deep learning model to construct a classification model for aggregate shape classification, effectively improving the recognition accuracy of flaky and elongated aggregates.
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