In this paper,the deep learning algorithm is used to segment the fine aggregate projection image,and the evaluation and analysis on the traditional threshold segmentation and three deep learn-ing network model algorithms(PSPNet,DeepLab V3+and U-Net)are conducted by comparing their segmentation results.At the same time,the results of grain size and gradation distribution of fine ag-gregate measured by two equivalent grain size calculation methods(single-sided projection method and double-sided projection method)were compared experimentally.The results show that the accuracy rate,recall rate,F-balance score and intersection ratio of U-Net network model in the deep learning model algorithm are 99.8％,88.1％,84.9％and 84.3％,respectively,which are superior to those of the control group model.For the single-grain segment fine aggregate with three different grain sizes,the deviation between the equivalent grain size Dd of fine aggregate calculated by double-sided projec-tion method and the actual fine aggregate size is 1.40％,2.10％and 3.12％,respectively.For the ag-gregate of mixed grain segment,the gradation distribution curve calculated by Dd is closer to the ex-perimental results of screening method,which has universal applicability.The results provide a new i-dea for the study of grain size and grain type parameters of fine aggregate.
fine aggregatethreshold segmentationdeep learning algorithmequivalent grain sizefine aggregate grain type parameters
NETL
NSTL
万方数据
