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- W4387013010 abstract "Abstract To improve the detection level of aggregate shape for automated road use, Per-Optuna-LightGBM model for aggregate shape classification is proposed. Collect aggregate images using industrial camera and extract 48 morphological feature parameters. A feature importance analysis method based on Spearman Correlation and Permutation Importance is proposed to remove redundant factors and select the feature parameters of aggregate morphology. Based on cross-validation, an optimized Optuna-LightGBM model is trained based on the constructed dataset. Compared with GS-XGBoost algorithm, the Optuna-LightGBM model can classify the shape of aggregates more accurately and efficiently. The accuracy value of the proposed model is 82.5%, which increased by 4% compared to before optimization. The proposed model can efficiently classify the shape of aggregates which meet the design requirements, also provide a certain foundation for automated classification of aggregate shapes." @default.
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- W4387013010 date "2023-09-01" @default.
- W4387013010 modified "2023-10-14" @default.
- W4387013010 title "Coarse Aggregate Shape Classification Method Based on Per-Optuna-LightGBM Model" @default.
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- W4387013010 doi "https://doi.org/10.1088/1742-6596/2589/1/012015" @default.
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