Current feature detection in particle flow fields was commonly measured by the particle image velocimetry(PIV)followed by manual identification of multiple consecutive frames,which is subject to some subjective errors.Therefore,a DC-UNet++ network for identifying the quasi-static regions in single-frame flow field images was proposed.Firstly,the particle flow field images formed by the impact experiments between the small ball and particle bed were captured by a high-speed charge coupled device(CCD)camera.Next,the raw images were used for PIV analysis and a dataset was produced.Then,the CNN model,UNet++ model and the proposed DC-UNet++ model were trained on multiple datasets to validate and analyze their feasibility and accuracy in detecting the quasi-static regions on a single frame image.Finally,the ability of the model to generalize over low velocity impact flow fields in non-transparent and transparent granular materials was discussed.The experimental results showed that the DC-UNet++ network achieved an accuracy of 87.76%and 72.91%on non-transparent and transparent granular materials,respectively.The DC-UNet++ network achieved the task of detecting the target features on a single image frame and still had a relatively accurate detection of features under complex flow fields in transparent granular materials.
NETL
NSTL
维普
万方数据
