首页|Automatic determination of 3D particle morphology from multiview images using uncertainty‐evaluated deep learning
Automatic determination of 3D particle morphology from multiview images using uncertainty‐evaluated deep learning
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NETL
NSTL
Wiley
Abstract Particle morphology is a crucial factor influencing the mechanical properties of granular materials particularly in infrastructure construction processes where accurate shape descriptors are essential. Accurately measuring three‐dimensional (3D) morphology has significant theoretical and practical value for exploring the multiscale mechanical properties of civil engineering materials. This study proposes a novel approach using multiview (two‐dimensional [2D]) particle images to efficiently predict 3D morphology, making real‐time aggregate quality analysis feasible. A 3D convolutional neural network (CNN) model is developed, which combines Monte Carlo dropout and attention mechanisms to achieve uncertainty‐evaluated predictions of 3D morphology. The model incorporates a convolutional block attention module, involving a two‐stage attention mechanism with channel attention and spatial attention, to further optimize feature representation and enhance the effectiveness of the attention mechanism. A new dataset comprising 18,000 images of 300 natural gravel and 300 blasted rock fragment particles is used for model training. The prediction accuracy and uncertainty of the proposed model are benchmarked against a range of alternative models including 2D CNN, 3D CNN, and 2D CNN with attention, in particular, to the influence of the number of input multiview particle images on the performance of the models for predicting various morphological parameters is explored. The results indicate that the proposed 3D CNN model with the attention mechanism achieves high prediction accuracy with an error of less than 10%. Whilst it exhibits initially greater uncertainty compared to other models due to its increased complexity, the model shows significant improvement in both accuracy and uncertainty as the number of training images is increased. Finally, residual challenges associated with the prediction of more complex particle angles and irregular shapes are also discussed.
Hongchen Liu、Huaizhi Su、Brian Sheil
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Hohai University||Hohai University||University of Cambridge
Hohai University||Hohai University||Hohai University
NETL
NSTL
Wiley
