Online segmentation and extraction of fine aggregate image based on deep learning technology
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
