Aggregate quality estimation method, quality estimation program, aggregate quality estimation device, ready-mixed concrete manufacturing method, and ready-mixed concrete manufacturing system
The method enhances aggregate quality prediction by using grayscale conversion and Otsu's binarization in image preprocessing, addressing accuracy and variability issues in machine learning models.
JP2026020891APending Publication Date: 2026-02-10MITSUBISHI UBE CEMENT CORP
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Patent Information
- Application Number
- JP2024122510
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Current Assignee / Owner
- Filing Date
- 2024-07-29
- Publication Date
- 2026-02-10
AI Technical Summary
Technical Problem
Existing prediction models for aggregate quality using machine learning suffer from decreased accuracy and increased variability due to variations in imaging environments and particle sizes not used during training.
Method used
A method involving image preprocessing through grayscale conversion followed by Otsu's binarization, combined with a prediction model constructed using machine learning, to accurately predict aggregate quality by analyzing image data.
Benefits of technology
Improves prediction accuracy and reduces variability in aggregate quality assessment by stabilizing the imaging environment and particle size considerations.
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Abstract
To improve prediction accuracy of a prediction model constructed by machine learning.SOLUTION: A first acquisition step of acquiring color captured image data obtained by capturing an image of a bone material; and a second acquisition step of sequentially performing a first process of converting the captured image data into grayscale data and a second process of binarizing the captured image data by Otsu's binarization on the captured image data. A quality prediction method of an aggregate, comprising a pre-processing step of generating input image data, a second acquisition step of acquiring input information including the input image data, and a prediction step of acquiring a physical property value according to the input information acquired in the second acquisition step by using a prediction model constructed in advance by machine learning so as to output the physical property value of the aggregate according to an input of the input information.SELECTED DRAWING: Figure 5
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