Aggregates Gradation Estimation Based on Three-Dimensional Texture Feature Extraction

Abstract:

In this paper， a 3D （three-dimensional） laser scanner was used to obtain texture data. Surface geometric parameters and 2D-wavelet-based multiscale parameters were proposed as inputs. Different parameters and classifiers were tested on eight types of mixtures with the standard gradation curve. The result shows that the MLP （multi-layered perceptron） performs best with the combination of the surface and 2D-wavelet parameters. The MLP regression model was used to estimate the gradation curves， and the R² was 0.849. The ablation experiments were used to analyze the contribution of different parameters. It is found that the 2D-wavelet energy parameters have more significant effects.